Emerging evidence shows that writing with AI assistance can change both the views people express and the opinions they hold. Yet, we lack a substantive understanding of behavioral and process-level changes in co-writing with AI that underlie the opinion-shaping power of these tools. We conducted a mixed-methods study, combining retrospective interviews with 19 participants about their co-writing experience with quantitative analysis tracing idea engagement in 1,291 AI co-writing sessions. Our analysis shows that engaging with the AI’s suggestions—reading them and deciding whether to accept them—becomes a central activity, taking away from more traditional processes of ideation and language generation. As writers often do not complete their own ideation before engaging with suggestions, the suggested ideas and opinions seeded directions that writers then elaborated on. At the same time, writers did not notice the AI’s influence and felt in control, as they—in principle—could always edit the final text. We term this shift Reactive Writing: an evaluation-first, suggestion-led writing practice that departs substantially from conventional composing in the presence of AI assistance and is highly vulnerable to AI-induced biases and opinion shifts.

Researchers have demonstrated that Automatic Speech Recognition (ASR) systems perform differently across demographic groups. In this work, we examined how subtitle errors affect evaluations of speakers and their content using a preregistered online experiment (N=207, US-based crowdworkers). Participants watched speakers with various accents deliver a talk in which the subtitles were accurate or error-prone. Our results indicate that error-prone subtitles consistently reduce both speaker and content evaluations for all speakers. We did not see disparate impact between the accent groups, controlling for subtitle quality. Taken together, though, the findings of this short paper imply that speakers with accents for which ASR systems perform poorly are likely to be further penalized by viewers with lower evaluations.

Social media platforms are increasingly adopting features that display crowdsourced context alongside posts, a technique pioneered by X’s Community Notes. These systems—which we term Crowdsourced Context Systems (CCS)—have the potential to reshape the information ecosystem as major platforms embrace them as alternatives to professional fact-checking. To understand the features and implications of these systems, we conduct a systematic literature review of existing CCS research (n=56) and analyze real-world CCS implementations. Based on our analysis, we develop a framework with two components. First, we present a theoretical model to conceptualize and define CCS. Second, we identify a design space encompassing six aspects: participation, inputs, curation, presentation, platform treatment, and transparency. We also surface normative implications of different CCS design and implementation choices. Our work integrates theoretical, design, and ethical perspectives to establish a foundation for future human-centered research on Crowdsourced Context Systems.

How are Reddit communities responding to AI-generated content?We explored this question through a large-scale analysis of subreddit community rules and their change over time.We collected the metadata and community rules for over 300, 000 public subreddits and measured the prevalence of rules governing AI.We labeled subreddits and AI rules according to existing taxonomies from the HCI literature and a new taxonomy we developed specifc to AI rules.While rules about AI are still relatively uncommon, the number of subreddits with these rules more than doubled over the course of a year.AI rules are more common in larger subreddits and communities focused on art or celebrity topics, and less common in those focused on social support.These rules often focus on AI images and evoke, as justifcation, concerns about quality and authenticity.Overall, our fndings illustrate the emergence of varied concerns about AI, in diferent community contexts.Platform designers and HCI researchers should heed these concerns if they hope to encourage community self-determination in the age of generative AI.We make our datasets public to enable future large-scale studies of community self-governance.

Generative AI is altering how we work, learn, communicate, and participate in online communities. How might online communities be changed by generative AI? To start addressing this question, we focused on online community moderators' experiences with AI-generated content (AIGC). We performed fifteen in-depth, semi-structured interviews with moderators of Reddit communities that restrict the use of AIGC. Our study finds that rules about AIGC are motivated by concerns about content quality, social dynamics, and governance challenges. Moderators fear that, without such rules, AIGC threatens to reduce their communities' utility and social value. We find that, despite the absence of robust tools for detecting AIGC, moderators were able to somewhat limit the disruption it caused by working with their communities to clarify norms. However, moderators found enforcing AIGC restrictions challenging, as they rely on time-intensive and inaccurate detection heuristics. Our results highlight the importance of supporting community autonomy and self-determination in the face of this sudden technological change, and suggest potential design solutions that may help.

This paper examines if large language models (LLMs) can help people write constructive comments on divisive social issues due to the difficulty of expressing constructive disagreement online. Through controlled experiments with 600 participants from India and the US, who reviewed and wrote constructive comments on threads related to Islamophobia and homophobia, we observed potential misalignment between how LLMs and humans perceive constructiveness in online comments. While the LLM was more likely to prioritize politeness and balance among contrasting viewpoints when evaluating constructiveness, participants emphasized logic and facts more than the LLM did. Despite these differences, participants rated both LLM-generated and human-AI co-written comments as significantly more constructive than those written independently by humans. Our analysis also revealed that LLM-generated comments integrated significantly more linguistic features of constructiveness compared to human-written comments. When participants used LLMs to refine their comments, the resulting comments were more constructive, more positive, less toxic, and retained the original intent. However, LLMs often distorted people's original views--especially when their stances were on a spectrum instead of being outright polarizing. Based on these findings, we discuss ethical and design considerations in using LLMs to facilitate constructive discourse online.

This paper examines the tensions between neighborhood gentrification and community surveillance posts on Nextdoor, a hyperlocal social media platform for neighborhoods. We created a privacy-preserving pipeline to gather research data from public Nextdoor posts in Atlanta, Georgia and filtered these to a dataset of 1,537 community surveillance posts. We developed a qualitative codebook to label observed patterns of community surveillance, and deploy a large language model to tag these posts at scale. Ultimately, we present an extensible and empirically-tested typology of the modes of community surveillance that occur on hyperlocal platforms. We find a complex relationship between community surveillance posts and neighborhood gentrification, which indicates that publicly disclosing information about perceived outsiders, especially for petty crimes, is most prevalent in gentrifying neighborhoods. Our empirical evidence inform critical perspectives which posit that community surveillance on platforms like Nextdoor can exclude and marginalize minoritized populations, particularly in gentrifying neighborhoods. Our findings carry broader implications for hyperlocal social platforms and their potential to amplify and exacerbate social tensions and exclusion.

Given large language models’ (LLMs) increasing integration into workplace software, it is important to examine how biases in the models may impact workers. For example, stylistic biases in the language suggested by LLMs may cause feelings of alienation and result in increased labor for individuals or groups whose style does not match. We examine how such writer-style bias impacts inclusion, control, and ownership over the work when co-writing with LLMs. In an online experiment, participants wrote hypothetical job promotion requests using either hesitant or self-assured auto-complete suggestions from an LLM and reported their subsequent perceptions. We found that the style of the AI model did not impact perceived inclusion. However, individuals with higher perceived inclusion did perceive greater agency and ownership, an effect more strongly impacting participants of minoritized genders. Feelings of inclusion mitigated a loss of control and agency when accepting more AI suggestions.

With the steady closure of local newspapers, news consumers increasingly turn to community forums and neighborhood apps to fill the information void. This study investigates how local online groups are perceived relative to more traditional local news outlets, and compares the benefits provided by each information source. Based on prior theoretical contributions, we develop a framework for measuring the benefits of local information on individual-level pro-community attitudes (attachment, knowledge, and civic attitudes.) In a field experiment (N=170), we asked frequent Facebook users living in four U.S. cities to start following local news pages or local online groups on Facebook for one month, and compared their perceptions of source quality and changes in pro-community attitudes. We find that posts from local news pages are perceived to be of higher quality than posts from local online groups. However, following local news pages or local groups did not lead to significant changes in pro-community attitudes during our study period. We discuss implications for the future study of local news in a changing media ecology.

If large language models like GPT-3 preferably produce a particular point of view, they may influence people’s opinions on an unknown scale. This study investigates whether a language-model-powered writing assistant that generates some opinions more often than others impacts what users write – and what they think. In an online experiment, we asked participants (N=1,506) to write a post discussing whether social media is good for society. Treatment group participants used a language-model-powered writing assistant configured to argue that social media is good or bad for society. Participants then completed a social media attitude survey, and independent judges (N=500) evaluated the opinions expressed in their writing. Using the opinionated language model affected the opinions expressed in participants’ writing and shifted their opinions in the subsequent attitude survey. We discuss the wider implications of our results and argue that the opinions built into AI language technologies need to be monitored and engineered more carefully.

From autocomplete and smart replies to video filters and deepfakes, we increasingly live in a world where communication between humans is augmented by artificial intelligence. AI often operates on behalf of a human communicator by recommending, suggesting, modifying, or generating messages to accomplish communication goals. We call this phenomenon AI-Mediated Communication (or AI-MC) [1, 4]. While AI-MC has the potential of making human communication more efficient, it impacts other aspects of our communication in ways that are not yet well understood. Over the last three years, my collaborators and I have been documenting the impact of AI-MC on communication outcomes, language use, interpersonal trust, and more. The talk will outline early experimental findings from this work, mostly led by Cornell and Stanford graduate students Maurice Jakesch, Hannah Mieczkowski, and Jess Hohenstein. For example, the research shows that AI-MC involvement can result in language shifting towards positivity [2, 7]; impact the evaluation of others [2, 4]; change the extent to which we take ownership over our messages [6]; and shift assignment of blame for communication outcomes [3]. Given the impact of AI-MC on interpersonal evaluations, the talk will also cover our recent research examining the (mostly false) heuristics humans use when evaluating whether text was written by AI [5]. Overall, AI-MC raises significant practical and ethical concerns as it stands to reshape human communication, calling for new approaches to the development and regulation of these technologies.

Widespread conspiracy theories may significantly impact our society. This paper focuses on the QAnon conspiracy theory, a consequential conspiracy theory that started on and disseminated successfully through social media. Our work characterizes how Reddit users who have participated in QAnon-focused subreddits engage in activities on the platform, especially outside their own communities. Using a large-scale Reddit moderation action against QAnon-related activities in 2018 as the source, we identified 13,000 users active in the early QAnon communities. We collected the 2.1 million submissions and 10.8 million comments posted by these users across all of Reddit from October 2016 to January 2021. The majority of these users were only active after the emergence of the QAnon conspiracy theory and decreased in activity after Reddit's 2018 QAnon ban. A qualitative analysis of a sample of 915 subreddits where the "QAnon-enthusiastic" users were especially active shows that they participated in a diverse range of subreddits, often of unrelated topics to QAnon. However, most of the users' submissions were concentrated in subreddits that have sympathetic attitudes towards the conspiracy theory, characterized by discussions that were pro-Trump, or emphasized unconstricted behavior (often anti-establishment and anti-interventionist). Further study of a sample of 1,571 of these submissions indicates that most consist of links from low-quality sources, bringing potential harm to the broader Reddit community. These results point to the likelihood that the activities of early QAnon users on Reddit were dedicated and committed to the conspiracy, providing implications on both platform moderation design and future research.

One of the key emerging roles of the YouTube platform is providing creators the ability to generate revenue from their content and interactions. Alongside tools provided directly by the platform, such as revenue-sharing from advertising, creators co-opt the platform to use a variety of off-platform monetization opportunities. In this work, we focus on studying and characterizing these alternative monetization strategies. Leveraging a large longitudinal YouTube dataset of popular creators, we develop a taxonomy of alternative monetization strategies and a simple methodology to detect their usage automatically. We then proceed to characterize the adoption of these strategies. First, we find that the use of external monetization is expansive and increasingly prevalent, used in 18% of all videos, with 61% of channels using one such strategy at least once. Second, we show that the adoption of these strategies varies substantially among channels of different kinds and popularity, and that channels that establish these alternative revenue streams often become more productive on the platform. Lastly, we investigate how potentially problematic channels -- those that produce Alt-lite, Alt-right, and Manosphere content -- leverage alternative monetization strategies, finding that they employ a more diverse set of such strategies significantly more often than a carefully chosen comparison set of channels. This finding complicates YouTube's role as a gatekeeper, since the practice of excluding policy-violating content from its native on-platform monetization may not be effective. Overall, this work provides an important step toward broadening the understanding of the monetary incentives behind content creation on YouTube.

Images are powerful. Visual information can attract attention, improve persuasion, trigger stronger emotions, and is easy to share and spread. We examine the characteristics of the popular images shared on Twitter as part of "Stop the Steal'', the widespread misinformation campaign during the 2020 U.S. election. We analyze the spread of the forty most popular images shared on Twitter as part of this campaign. Using a coding process, we categorize and label the images according to their type, content, origin, and role, and perform a mixed-method analysis of these images' spread on Twitter. Our results show that popular images include both photographs and text rendered as image. Only very few of these popular images included alleged photographic evidence of fraud; and none of the popular photographs had been manipulated. Most images reached a significant portion of their total spread within several hours from their first appearance, and both popular- and less-popular accounts were involved in various stages of their spread.

COVID-19 has been a sustained and global crisis with a strong continual impact on daily life. Staying accurately informed about COVID-19 has been key to personal and communal safety, especially for essential workers---individuals whose jobs have required them to go into work throughout the pandemic---as their employment has exposed them to higher risks of contracting the virus. Through 14 semi-structured interviews, we explore how essential workers across industries navigated the COVID-19 information landscape to get up-to-date information in the early months of the pandemic. We find that essential workers living through a sustained crisis have a broad set of information needs. We summarize these needs in a framework that centers 1) fulfilling job requirements, 2) assessing personal risk, and 3) keeping up with crisis news coverage. Our findings also show that the sustained nature of COVID-19 crisis coverage led essential workers to experience breaking points and develop coping strategies. Additionally, we show how workplace communications may act as a mediating force in this process: lack of adequate information in the workplace caused workers to struggle with navigating a contested information landscape, while consistent updates and information exchanges at work could ease the stress of information overload. Our findings extend the crisis informatics field by providing contextual knowledge about the information needs of essential workers during a sustained crisis.

Political organizations worldwide keep innovating their use of social media technologies. In the 2019 Indian general election, organizers used a network of WhatsApp groups to manipulate Twitter trends through coordinated mass postings. We joined 600 WhatsApp groups that support the Bharatiya Janata Party, the right-wing party that won the general election, to investigate these campaigns. We found evidence of 75 hashtag manipulation campaigns in the form of mobilization messages with lists of pre-written tweets. Building on this evidence, we estimate the campaigns' size, describe their organization and determine whether they succeeded in creating controlled social media narratives. Our findings show that the campaigns produced hundreds of nationwide Twitter trends throughout the election. Centrally controlled but voluntary in participation, this hybrid configuration of technologies and organizational strategies shows how profoundly online tools transform campaign politics. Trend alerts complicate the debates over the legitimate use of digital tools for political participation and may have provided a blueprint for participatory media manipulation by a party with popular support.

The COVID-19 outbreak has resulted in a worldwide public health crisis. In such times of crisis, access to relevant and accurate information is critical. For many people in China, domestic social media platforms such as WeChat and Weibo have become dominant sources of COVID-19-related information and news. People have to evaluate the trustworthiness of COVID-19-related information and make sharing decisions using platforms that have to contend with government censorship policies, astroturfers, and other government interventions. We interviewed 33 Chinese WeChat users to understand how individuals were seeking COVID-19-related information and how they identified and evaluated specific COVID-19-related misinformation. This work exposes how COVID-19-related content with "positive energy" was prevalent on social media in China. A significant number of interviewees exhibited a willingness to prioritize information valence over veracity when evaluating and sharing content with others. Further, the work revealed how Chinese citizens' understanding of information ecosystems played an important role in their attitudes towards censorship and official media, and also influenced their evaluation of domestic and international information during a global crisis.

AI-Mediated Communication (AI-MC) is interpersonal communication that involves an artificially intelligent system that can modify, augment, or even generate content to achieve communicative and relational goals. AI-MC is increasingly involved in human communication and has the potential to impact core aspects of human communication, such as language production, interpersonal perception and task performance. Through a between-subjects experimental design we examine how these processes are influenced when integrating AI-generated language in the form of suggested text responses (Google's smart replies) into a text-based referential communication task. Our study replicates and extends the impacts of a positivity bias in AI-generated language and introduces the adjacency pair framework into the study of AI-MC. We also find preliminary yet mixed evidence to suggest that AI-generated language has the potential to undermine some dimensions of interpersonal perception, such as social attraction. This study contributes important concepts for future work in AI-MC and offers findings with implications for the design of AI systems in human-to-human communication.

Social media provides a critical communication platform for political figures, but also makes them easy targets for harassment. In this paper, we characterize users who adversarially interact with political figures on Twitter using mixed-method techniques. The analysis is based on a dataset of 400 thousand users' 1.2 million replies to 756 candidates for the U.S. House of Representatives in the two months leading up to the 2018 midterm elections. We show that among moderately active users, adversarial activity is associated with decreased centrality in the social graph and increased attention to candidates from the opposing party. When compared to users who are similarly active, highly adversarial users tend to engage in fewer supportive interactions with their own party's candidates and express negativity in their user profiles. Our results can inform the design of platform moderation mechanisms to support political figures countering online harassment.

The social media environment in China has become the dominant source of information and news over the past decade. This news environment has naturally suffered from challenges related to mis- and dis-information, encumbered by an increasingly complex landscape of factors and players including social media services, fact-checkers, censorship policies, and astroturfing. Interviews with 44 Chinese WeChat users were conducted to understand how individuals perceive misinformation and how it impacts their news consumption practices. Overall, this work exposes the diverse attitudes and coping strategies that Chinese users employ in complex social media environments. Due to the complex nature of censorship in China and participants' lack of understanding of censor-ship, they expressed varied opinions about its influence on the credibility of online information sources. Further, although most participants claimed that their opinions would not be easily swayed by astroturfers, many admitted that they could not effectively distinguish astroturfers from ordinary Internet users. Participants' inability to make sense of comments found online lead many participants to hold pro-censorship attitudes: the Government's Dividend.

We are entering an era of AI-Mediated Communication (AI-MC) where interpersonal communication is not only mediated by technology, but is optimized, augmented, or generated by artificial intelligence. Our study takes a first look at the potential impact of AI-MC on online self-presentation. In three experiments we test whether people find Airbnb hosts less trustworthy if they believe their profiles have been written by AI. We observe a new phenomenon that we term the Replicant Effect: Only when participants thought they saw a mixed set of AI- and human-written profiles, they mistrusted hosts whose profiles were labeled as or suspected to be written by AI. Our findings have implications for the design of systems that involve AI technologies in online self-presentation and chart a direction for future work that may upend or augment key aspects of Computer-Mediated Communication theory.

Trust facilitates cooperation and supports positive outcomes in social groups, including member satisfaction, information sharing, and task performance. Extensive prior research has examined individuals' general propensity to trust, as well as the factors that contribute to their trust in specific groups. Here, we build on past work to present a comprehensive framework for predicting trust in groups. By surveying 6,383 Facebook Groups users about their trust attitudes and examining aggregated behavioral and demographic data for these individuals, we show that (1) an individual's propensity to trust is associated with how they trust their groups, (2) smaller, closed, older, more exclusive, or more homogeneous groups are trusted more, and (3) a group's overall friendship-network structure and an individual's position within that structure can also predict trust. Last, we demonstrate how group trust predicts outcomes at both individual and group level such as the formation of new friendship ties.

As any savvy online shopper knows, second-hand peer-to-peer marketplaces are filled with images of mixed quality. How does image quality impact marketplace outcomes, and can quality be automatically predicted? In this work, we conducted a large-scale study on the quality of user-generated images in peer-to-peer marketplaces. By gathering a dataset of common second-hand products (≈75,000 images) and annotating a subset with human-labeled quality judgments, we were able to model and predict image quality with decent accuracy (≈87%). We then conducted two studies focused on understanding the relationship between these image quality scores and two marketplace outcomes: sales and perceived trustworthiness. We show that image quality is associated with higher likelihood that an item will be sold, though other factors such as view count were better predictors of sales. Nonetheless, we show that high quality user-generated images selected by our models outperform stock imagery in eliciting perceptions of trust from users. Our findings can inform the design of future marketplaces and guide potential sellers to take better product images.

Recent years have witnessed the flourishing of podcasts, a unique type of audio medium. Prior work on podcast content modeling focused on analyzing Automatic Speech Recognition outputs, which ignored vocal, musical, and conversational properties (e.g., energy, humor, and creativity) that uniquely characterize this medium. In this paper, we present an Adversarial Learning-based Podcast Representation (ALPR) that captures non-textual aspects of podcasts. Through extensive experiments on a large-scale podcast dataset (88,728 episodes from 18,433 channels), we show that (1) ALPR significantly outperforms the state-of-the-art features developed for music and speech in predicting theseriousness andenergy of podcasts, and (2) incorporating ALPR significantly improves the performance of topic-based podcast-popularity prediction. Our experiments also reveal factors that correlate with podcast popularity.

Rich engagement data can shed light on how people interact with online content and how such interactions may be determined by the content of the page. In this work, we investigate a specific type of interaction, backtracking, which refers to the action of scrolling back in a browser while reading an online news article. We leverage a dataset of close to 700K instances of more than 15K readers interacting with online news articles, in order to characterize and predict backtracking behavior. We first define different types of backtracking actions. We then show that “full” backtracks, where the readers eventually return to the spot at which they left the text, can be predicted by using features that were previously shown to relate to text readability. This finding highlights the relationship between backtracking and readability and suggests that backtracking could help assess readability of content at scale.

People's content choices are ideally driven by their intentions, aspirations, and plans. However, in reality, choices may be modulated by recommendation systems which are typically trained to promote popular items and to reinforce users' historical behavior. As a result, the utility and user experience of content consumption can be affected implicitly and undesirably. To study this problem, we conducted a 2 × 2 randomized controlled field experiment (105 urban college students) to compare the effects of intention informed recommendations with classical intention agnostic systems. The study was conducted in the context of spoken word web content (podcasts) which is often consumed through subscription sites or apps. We modified a commercial podcast app to include (1) a recommender that takes into account users' stated intentions at onboarding, and (2) a Collaborative Filtering (CF) recommender during daily use. Our study suggests that: (1) intention-aware recommendations can significantly raise users' interactions (subscriptions and listening) with channels and episodes related to intended topics by over 24%, even if such a recommender is only used during onboarding, and (2) the CF-based recommender doubles users' explorations on episodes from not-subscribed channels and improves satisfaction for users onboarded with the intention-aware recommender.

Indirect resource exchange (IRE), where individuals share physical items with one another but do not receive direct benefits (e.g. payment), has the potential to increase communities' access to resources, reduce consumption and waste, and bootstrap social ties. Although social technologies could play a key role in realizing this potential, significant barriers have emerged to the adoption of IRE services, including concerns related to trust, reciprocity, and coordination. To explore these issues, we designed and iterated on a concept called ShareBox, a system that enables IRE through a smart lockbox. We developed ShareBox as a technology probe following a set of design guidelines including: creating a physical-virtual system, enabling asynchronous and anonymous exchange, allowing for low-entry-barrier interactions, and emphasizing affordability and flexibility. We explore the benefits and trade-offs of these design guidelines through short deployments and semi-structured interviews with community members, and present findings that highlight both the potential and the remaining challenges of our design.

Local community ties are an important social resource, but research shows that these ties have been declining. The social significance of location information offers an opportunity address this decline and support local community building. Through this research, we aim to understand if and how passive location sharing might be socially beneficial for communities. We conducted a deployment of MoveMeant, a location awareness app, across three different communities. Following a research through design approach, we conducted 45 interviews with users of the system and community leaders. The findings suggest that communities face issues related to lack of awareness, cohesion, and identity. We show that the app can help increase awareness of important community resources. At the same time, the findings also show a negative effect of surfacing divisions in a community, which we discuss as a intermediate, perceptual step that may contribute to the amplification effect of technology.

Max Grusky, Mor Naaman, Yoav Artzi. Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers). 2018.

We build on the increasing availability of Virtual Reality (VR) devices and Web technologies to conduct behavioral experiments in VR using crowdsourcing techniques. A new recruiting and validation method allows us to create a panel of eligible experiment participants recruited from Amazon Mechanical Turk. Using this panel, we ran three different crowdsourced VR experiments, each reproducing one of three VR illusions: place illusion, embodiment illusion, and plausibility illusion. Our experience and worker feedback on these experiments show that conducting Web-based VR experiments using crowdsourcing is already feasible, though some challenges---including scale---remain. Such crowdsourced VR experiments on the Web have the potential to finally support replicable VR experiments with diverse populations at a low cost.

Website measures of engagement captured from millions of users, such as in-page scrolling and viewport position, can provide deeper understanding of attention than possible with simpler measures, such as dwell time. Using data from 1.2M news reading sessions, we examine and evaluate three increasingly sophisticated models of sub-document attention computed from viewport time, the time a page component is visible on the user display. Our modeling incorporates prior eye-tracking knowledge about onscreen reading, and we validate it by showing how, when used to estimate user reading rate, it aligns with known empirical measures. We then show how our models reveal an interaction between article topic and attention to page elements. Our approach supports refined large-scale measurement of user engagement at a level previously available only from lab-based eye-tracking studies.

In recent years, several mobile applications allowed individuals to anonymously share information with friends and contacts, without any persistent identity marker. The functions of these "tie-based" anonymity services may be notably different than other social media services. We use semi-structured interviews to qualitatively examine motivations, practices and perceptions in two tie-based anonymity apps: Secret (now defunct, in the US) and Mimi (in China). Among the findings, we show that: (1) while users are more comfortable in self-disclosure, they still have specific practices and strategies to avoid or allow identification; (2) attempts for deidentification of others are prevalent and often elaborate; and (3) participants come to expect both negativity and support in response to posts. Our findings highlight unique opportunities and potential benefits for tie-based anonymity apps, including serving disclosure needs and social probing. Still, challenges for making such applications successful, for example the prevalence of negativity and bullying, are substantial.

Awareness of and connections to a local community are important for building social capital, sharing resources, and providing physical support, but have been elusive to create in dense urban environments. We describe the design and implementation of MoveMeant, a system aimed to increase local community awareness through shared location traces. MoveMeant securely uses anonymized location data generated automatically by mobile devices to display aggregate, community-level location data. We report findings from interviews with residents in the Bronx, New York City who participated in a deployment of MoveMeant over a 6-week period. Our findings show that people use the anonymous information to make judgments about the people and places in their community, while opting to reveal their identity for third places where there is an opportunity to connect socially.

When people share updates with their friends on Facebook they have varying expectations for the feedback they will receive. In this study, we quantitatively examine the factors contributing to feedback expectations and the potential outcomes of expectation fulfillment. We conducted two sets of surveys: one asking people about their feedback expectations immediately after posting on Facebook and the other asking how the amount of feedback received on a post matched the participant's expectations. Participants were more likely to expect feedback on content they evaluated as more important, and to a lesser extent more personal. Expectations also depended on participants' age, gender, and level of activity on Facebook. When asked about feedback expectations from specific friends, participants were more likely to expect feedback from closer friends, but expectations varied considerably based on recency of communication, geographical proximity, and the type of relationship (e.g. family, co-worker). Finally, receiving more feedback relative to expectations correlated with a greater feeling of connectedness to one's Facebook friends. The findings suggest implications for the theory and the design of social network sites.

Online peer-to-peer platforms like Airbnb allow hosts to list a property (e.g. a house, or a room) for short-term rentals. In this work, we examine how hosts describe themselves on their Airbnb profile pages. We use a mixed-methods study to develop a categorization of the topics that hosts self-disclose in their profile descriptions, and show that these topics differ depending on the type of guest engagement expected. We also examine the perceived trustworthiness of profiles using topic-coded profiles from 1,200 hosts, showing that longer self-descriptions are perceived to be more trustworthy. Further, we show that there are common strategies (a mix of topics) hosts use in self-disclosure, and that these strategies cause differences in perceived trustworthiness scores. Finally, we show that the perceived trustworthiness score is a significant predictor of host choice--especially for shorter profiles that show more variation. The results are consistent with uncertainty reduction theory, reflect on the assertions of signaling theory, and have important design implications for sharing economy platforms, especially those facilitating online-to-offline social exchange.

Peer-to-peer indirect exchange services, such as sharing sites that facilitate the lending and borrowing of physical goods among neighbors (such as NeighborGoods and Peerby), have not been as widely adopted as direct exchange systems, such as peer-to-peer platforms that facilitate the exchange goods and services for money (such as Uber and Airbnb). In order to understand contributing factors to this lack of adoption, we examined attitudes towards and usage of peer-to-peer resource-sharing sites among 37 residents of New York City, 9 of whom had previously used a peer-to-peer sharing site. In addition, to more deeply understand the role of trust on willingness to lend, we also conducted a survey with 195 respondents. Our findings show that people expressed concerns about violating norms of the kinds of objects suitable for sharing, about potential risks involved with entrusting a possession to somebody else, and about a dearth of available items that would be useful. Building upon previous technology acceptance models, critical mass theory, and prior research on peer economies, we propose a technology acceptance model for indirect exchange systems that includes generalized trust and ease of coordination.

The asynchronous nature of communications on social network sites creates a unique opportunity for studying how posting content interacts with individuals' engagement. This study focuses on the behavioral changes occurring hours before and after contribution to better understand the changing needs and preferences of contributors. Using observational data analysis of individuals' activity on Facebook, we test hypotheses regarding the motivations for site visits, changes in the distribution of attention to content, and shifts in decisions to interact with others. We find that after posting content people are intrinsically motivated to visit the site more often, are more attentive to content from friends (but not others), and choose to interact more with friends (in large part due to reciprocity). In addition, contributors are more active on the site hours before posting and remain more active for less than a day afterwards. Our study identifies a unique pattern of engagement that accompanies contribution and can inform the design of social network sites to better support contributors.

Self-disclosure is rewarding and provides significant benefits for individuals, but it also involves risks, especially in social media settings. We conducted an online experiment to study the relationship between content intimacy and willingness to self-disclose in social media, and how identification (real name vs. anonymous) and audience type (social ties vs. people nearby) moderate that relationship. Content intimacy is known to regulate self-disclosure in face-to-face communication: people self-disclose less as content intimacy increases. We show that such regulation persists in online social media settings. Further, although anonymity and an audience of social ties are both known to increase self-disclosure, it is unclear whether they (1) increase self-disclosure baseline for content of all intimacy levels, or (2) weaken intimacy's regulation effect, making people more willing to disclose intimate content. We show that intimacy always regulates self-disclosure, regardless of settings. We also show that anonymity mainly increases self-disclosure baseline and (sometimes) weakens the regulation. On the other hand, an audience of social ties increases the baseline but strengthens the regulation. Finally, we demonstrate that anonymity has a more salient effect on content of negative valence.The results are critical to understanding the dynamics and opportunities of self-disclosure in social media services that vary levels of identification and types of audience.

People share in social media an overwhelming amount of content from real-world events. These events range from major global events like an uprising or an earthquake, to local events and emergencies such as a fire or a parade; from media events like the Oscar's, to events that enjoy little media coverage such as a conference or a music concert. This shared media represents an important part of our society, culture and history. At the same time, this social media content is still fragmented across services, hard to find, and difficult to consume and understand.

We propose a new user-centric recommendation model, called Immersive Recommendation, that incorporates cross-platform and diverse personal digital traces into recommendations. Our context-aware topic modeling algorithm systematically profiles users' interests based on their traces from different contexts, and our hybrid recommendation algorithm makes high-quality recommendations by fusing users' personal profiles, item profiles, and existing ratings. Specifically, in this work we target personalized news and local event recommendations for their utility and societal importance. We evaluated the model with a large-scale offline evaluation leveraging users' public Twitter traces. In addition, we conducted a direct evaluation of the model's recommendations in a 33-participant study using Twitter, Facebook and email traces. In the both cases, the proposed model showed significant improvement over the state-of-the-art algorithms, suggesting the value of using this new user-centric recommendation model to improve recommendation quality, including in cold-start situations.

Social media users share billions of items per year, only a small fraction of which is geotagged. We present a data-driven approach for identifying non-geotagged content items that can be associated with a hyper-local geographic area by modeling the location distributions of n-grams that appear in the text. We explore the trade-off between accuracy and coverage of this method. Further, we explore differences across content received from multiple platforms and devices, and show, for example, that content shared via different sources and applications produces significantly different geographic distributions, and that it is preferred to model and predict location for items according to their source. Our findings show the potential and the bounds of a data-driven approach to assigning location data to short social media texts, and offer implications for all applications that use data-driven approaches to locate content.

This paper uses qualitative interviews with 26 transnational migrants in New York City to analyze socio-technical practices related to online identity work. We focus specifically on the use of Facebook, where benefits included keeping in touch with friends and family abroad and documenting everyday urban life. At the same time, many participants also reported experiences of fatigue, socio-cultural tensions and concerns about maintaining a sense of personal privacy. These experiences highlight how transnational practices complicate context collapse, where the geographic dispersal of participants' personal networks renders visible conflicts of 'flattened' online networks. Our findings also suggest a kind of technology-enabled code-switching, where transnational migrants leverage social media to perform identities that alternate between communities, nationalities and geographies. This analysis informs HCI research on transnationalism and technological practices, as well as the complexities of online identity work in terms of shifting social and spatial contexts.

On-demand mobile workforce applications match physical world tasks and willing workers. These systems offer to help conserve resources, streamline courses of action, and increase market efficiency for micro- and mid-level tasks, from verifying the existence of a pothole to walking a neighbor's dog. This study reports on the motivations and experiences of individuals who regularly complete physical world tasks posted in on-demand mobile workforce marketplaces. Data collection included semi-structured interviews with members (workers) of two different services. The analysis revealed the main drivers for participating in an on-demand mobile workforce, including desires for monetary compensation and control over schedules and task selection. We also reveal main reasons for task selection, which involve situational factors, convenient physical locations, and task requester profile information. Finally, we discuss the key characteristics of the most worthwhile tasks and offer implications for novel crowdsourcing systems for physical world tasks.

Social media is already a fixture for reporting for many journalists, especially around breaking news events where non-professionals may already be on the scene to share an eyewitness report, photo, or video of the event. At the same time, the huge amount of content posted in conjunction with such events serves as a challenge to finding interesting and trustworthy sources in the din of the stream. In this paper we develop and investigate new methods for filtering and assessing the verity of sources found through social media by journalists. We take a human centered design approach to developing a system, SRSR ("Seriously Rapid Source Review"), informed by journalistic practices and knowledge of information production in events. We then used the system, together with a realistic reporting scenario, to evaluate the filtering and visual cue features that we developed. Our evaluation offers insights into social media information sourcing practices and challenges, and highlights the role technology can play in the solution.

Social media platforms such as Twitter garner significant attention from very large audiences in response to real-world events. Automatically establishing who is participating in information production or conversation around events can improve event content consumption, help expose the stakeholders in the event and their varied interests, and even help steer subsequent coverage of an event by journalists. In this paper, we take initial steps towards building an automatic classifier for user types on Twitter, focusing on three core user categories that are reflective of the information production and consumption processes around events: organizations, journalists/media bloggers, and ordinary individuals. Exploration of the user categories on a range of events shows distinctive characteristics in terms of the proportion of each user type, as well as differences in the nature of content each shared around the events.

By examining the information practices of a punk-rock subculture, we investigate the limits of social media systems, particularly limits exposed by practices of secrecy. Looking at the exchange of information about "underground" shows, we use qualitative interviews to examine uses of social media among fans. This initial analysis centers on understanding the tactical practices of information and technology to avoid police detection, particularly by comparing uses of more traditional online forums, such as message boards, with social network sites, such as Facebook. Understanding the uses and preferences for distinct technologies sheds light on how localized social context drives technological use. These findings are furthermore useful in their implications for design of applications sensitive to granular needs of users for secrecy.

As the amount of social video content captured at physical-world events, and shared online, is rapidly increasing, there is a growing need for robust methods for organization and presentation of the captured content. In this work, we significantly extend prior work that examined automatic detection of videos from events that were captured at the same time, i.e. "overlapping". We go beyond finding pairwise matches between video clips and describe the construction of scenes, or sets of multiple overlapping videos, each scene presenting a coherent moment in the event. We test multiple strategies for scene construction, using a greedy algorithm to create a mapping of videos into scenes, and a clustering refinement step to increase the precision of each scene. We evaluate the strategies in multiple settings and show that a greedy and clustering approach results in best possible balance between recall and precision for all settings.

User-contributed Web data contains rich and diverse information about a variety of events in the physical world, such as shows, festivals, conferences and more. This information ranges from known event features (e.g., title, time, location) posted on event aggregation platforms (e.g., Last.fm events, EventBrite, Facebook events) to discussions and reactions related to events shared on different social media sites (e.g., Twitter, YouTube, Flickr). In this paper, we focus on the challenge of automatically identifying user-contributed content for events that are planned and, therefore, known in advance, across different social media sites. We mine event aggregation platforms to extract event features, which are often noisy or missing. We use these features to develop query formulation strategies for retrieving content associated with an event on different social media sites. Further, we explore ways in which event content identified on one social media site can be used to retrieve additional relevant event content on other social media sites. We apply our strategies to a large set of user-contributed events, and analyze their effectiveness in retrieving relevant event content from Twitter, YouTube, and Flickr.

This work explores the intersection between infographics and games by examining how to embed meaningful visual analytic interactions into game mechanics that in turn impact user behavior around a data-driven graphic. In contrast to other methods of narrative visualization, games provide an alternate method for structuring a story, not bound by a linear arrangement but still providing structure via rules, goals, and mechanics of play. We designed two different versions of a game-y infographic, Salubrious Nation, and compared them to a non-game-y version in an online experiment. We assessed the relative merits of the game-y approach of presentation in terms of exploration of the visualization, insights and learning, and enjoyment of the experience. Based on our results, we discuss some of the benefits and drawbacks of our designs. More generally, we identify challenges and opportunities for further exploration of this new design space.

We investigate the breaking of ties between individuals in the online social network of Twitter, a hugely popular social media service. Building on sociology concepts such as strength of ties, embeddedness, and status, we explore how network structure alone influences tie breaks - the common phenomena of an individual ceasing to "follow" another in Twitter's directed social network. We examine these relationships using a dataset of 245,586 Twitter "follow" edges, and the persistence of these edges after nine months. We show that structural properties of individuals and dyads at Time 1 have a significant effect on the existence of edges at Time 2, and connect these findings to the social theories that motivated the study.

With the growth in sociality and interaction around online news media, news sites are increasingly becoming places for communities to discuss and address common issues spurred by news articles. The quality of online news comments is of importance to news organizations that want to provide a valuable exchange of community ideas and maintain credibility within the community. In this work we examine the complex interplay between the needs and desires of news commenters with the functioning of different journalistic approaches toward managing comment quality. Drawing primarily on newsroom interviews and reader surveys, we characterize the comment discourse of SacBee.com, discuss the relationship of comment quality to both the consumption and production of news information, and provide a description of both readers' and writers' motivations for usage of news comments. We also examine newsroom strategies for dealing with comment quality as well as explore tensions and opportunities for value-sensitive innovation within such online communities.

The relationship between social sharing of emotions, social networks and social ties is an ongoing topic of research. Such sharing of emotions occurs frequently in "social awareness streams" platforms like Twitter and Facebook. We use Twitter to address research questions about the association of properties of a user's network, such as size and density, with expression of emotion in the user's Twitter posts. Our analysis suggests that expression of emotion can explain some of the variance in users' Twitter networks, and that the use of emotion in interactions between users is a strong explaining factor.

Twitter, Facebook, and other related systems that we call social awareness streams are rapidly changing the information and communication dynamics of our society. These systems, where hundreds of millions of users share short messages in real time, expose the aggregate interests and attention of global and local communities. In particular, emerging temporal trends in these systems, especially those related to a single geographic area, are a significant and revealing source of information for, and about, a local community. This study makes two essential contributions for interpreting emerging temporal trends in these information systems. First, based on a large dataset of Twitter messages from one geographic area, we develop a taxonomy of the trends present in the data. Second, we identify important dimensions according to which trends can be categorized, as well as the key distinguishing features of trends that can be derived from their associated messages. We quantitatively examine the computed features for different categories of trends, and establish that significant differences can be detected across categories. Our study advances the understanding of trends on Twitter and other social awareness streams, which will enable powerful applications and activities, including user-driven real-time information services for local communities.

In this work we examine the characteristics of social activity and patterns of communication on Twitter, a prominent example of the emerging class of communication systems we call "social awareness streams." We use system data and message content from over 350 Twitter users, applying human coding and quantitative analysis to provide a deeper understanding of the activity of individuals on the Twitter network. In particular, we develop a content-based categorization of the type of messages posted by Twitter users, based on which we examine users' activity. Our analysis shows two common types of user behavior in terms of the content of the posted messages, and exposes differences between users in respect to these activities.

Social media sites (e.g., Flickr, YouTube, and Facebook) are a popular distribution outlet for users looking to share their experiences and interests on the Web. These sites host substantial amounts of user-contributed materials (e.g., photographs, videos, and textual content) for a wide variety of real-world events of different type and scale. By automatically identifying these events and their associated user-contributed social media documents, which is the focus of this paper, we can enable event browsing and search in state-of-the-art search engines. To address this problem, we exploit the rich "context" associated with social media content, including user-provided annotations (e.g., title, tags) and automatically generated information (e.g., content creation time). Using this rich context, which includes both textual and non-textual features, we can define appropriate document similarity metrics to enable online clustering of media to events. As a key contribution of this paper, we explore a variety of techniques for learning multi-feature similarity metrics for social media documents in a principled manner. We evaluate our techniques on large-scale, real-world datasets of event images from Flickr. Our evaluation results suggest that our approach identifies events, and their associated social media documents, more effectively than the state-of-the-art strategies on which we build.

Abstract In recent years we have witnessed a significant growth of social‐computing communities—online services in which users share information in various forms. As content contributions from participants are critical to the viability of these communities, it is important to understand what drives users to participate and share information with others in such settings. We extend previous literature on user contribution by studying the factors that are associated with various forms of participation in a large online photo‐sharing community. Using survey and system data, we examine four different forms of participation and consider the differences between these forms. We build on theories of motivation to examine the relationship between users' participation and their motivations with respect to their tenure in the community. Amongst our findings, we identify individual motivations (both extrinsic and intrinsic) that underpin user participation, and their effects on different forms of information sharing; we show that tenure in the community does affect participation, but that this effect depends on the type of participation activity. Finally, we demonstrate that tenure in the community has a weak moderating effect on a number of motivations with regard to their effect on participation. Directions for future research, as well as implications for theory and practice, are discussed.

What is the future of digital imaging? Mobile imaging technologies have been changing rapidly and will continue to do so. We explore new developments in cameraphone photography with the goal of improving the design of the next generation of mobile imaging devices. We equipped 26 diverse participants with cameraphones, photo uploading and sharing software, and access to online photo-accounts for 3–5 months. This study allowed us to identify emerging practices in mobile photoware. We report on new and continuing practices across the lifespan of photos in this new imaging environment, including image capture, upload, annotation, archiving, sharing, and viewing. Based on these results, we develop design criteria and implications for designers and makers of mobile devices, mobile imaging and sharing software, and desktop and online photo software.

We describe a system for synchronization and organization of user-contributed content from live music events. We start with a set of short video clips taken at a single event by multiple contributors, who were using a varied set of capture devices. Using audio fingerprints, we synchronize these clips such that overlapping clips can be displayed simultaneously. Furthermore, we use the timing and link structure generated by the synchronization algorithm to improve the findability and representation of the event content, including identifying key moments of interest and descriptive text for important captured segments of the show. We also identify the preferred audio track when multiple clips overlap. We thus create a much improved representation of the event that builds on the automatic content match. Our work demonstrates important principles in the use of content analysis techniques for social media content on the Web, and applies those principles in the domain of live music capture.

We describe an approach for extracting semantics for tags, unstructured text-labels assigned to resources on the Web, based on each tag's usage patterns. In particular, we focus on the problem of extracting place semantics for tags that are assigned to photos on Flickr, a popular-photo sharing Web site that supports location (latitude/longitude) metadata for photos. We propose the adaptation of two baseline methods, inspired by well-known burst-analysis techniques, for the task; we also describe two novel methods, TagMaps and scale-structure identification. We evaluate the methods on a subset of Flickr data. We show that our scale-structure identification method outperforms existing techniques and that a hybrid approach generates further improvements (achieving 85% precision at 81% recall). The approach and methods described in this work can be used in other domains such as geo-annotated Web pages, where text terms can be extracted and associated with usage patterns.

We designed and iterated on a photo browsing application for high-end mobile phones. The application, Zurfer, supports viewing of photos from the user, their contacts, and the general user population. Photos are organized using a channel metaphor, driven by multiple dimensions: social, spatial and topical. Zurfer was deployed to over 500 users; extensive user research was conducted with nine participants. The data from the deployment and the study exposes general themes of mobile application use, as well as requirements for mobile applications in the photos domain, mobile social applications, and entertainment-driven mobile applications.

We examine tagging behavior on Flickr, a public photo-sharing website. We build on previous qualitative research that exposed a taxonomy of tagging motivations, as well as on social presence research. The motivation taxonomy suggests that motivations for tagging are tied to the intended target audience of the tags --- the users themselves, family and friends, or the general public. Using multiple data sources, including a survey and independent system data, we examine which motivations are associated with tagging level, and estimate the magnitude of their contribution. We find that the levels of the Self and Public motivations, together with social presence indicators, are positively correlated with tagging level; Family & Friends motivations are not significantly correlated with tagging. The findings and the use of survey method carry implications for designers of tagging and other social systems on the web.

The World Wide Web has become the world's largest networked information resource, but references to geographical locations remain unstructured and typically implicit in nature. This lack of explicit spatial knowledge within the Web makes it difficult to service user needs for location-specific information. At present, spatial knowledge is hidden in many small information fragments such as addresses on Web pages, annotated photos with GPS co-ordinates, geographic mapping applications, and geotags in user-generated content. Several emerging formats that primarily or secondarily include location metadata, like GeoRSS, KML, and microformats, aim to improve this state of affairs. However, the question remains how to extract, index, mine, find, view, mashup, and exploit Web content using its location semantics. This work-shop brings together researchers from academia and industry labs to discuss and present the latest results and trends in all facets of the relationships between location concepts and Web information.

Can we leverage the community-contributed collections of rich media on the web to automatically generate representative and diverse views of the world’s landmarks? We use a combination of context- and content-based tools to generate representative sets of images for location-driven features and landmarks, a common search task. To do that, we using location and other metadata, as well as tags associated with images, and the images ’ visual features. We present an approach to extracting tags that represent landmarks. We show how to use unsupervised methods to extract representative views and images for each landmark. This approach can potentially scale to provide better search and representation for landmarks, worldwide. We evaluate the system in the context of image search using a real-life dataset of 110,000 images from the San Francisco area.

As sharing personal media online becomes easier and widely spread, new privacy concerns emerge - especially when the persistent nature of the media and associated context reveals details about the physical and social context in which the media items were created. In a first-of-its-kind study, we use context-aware camerephone devices to examine privacy decisions in mobile and online photo sharing. Through data analysis on a corpus of privacy decisions and associated context data from a real-world system, we identify relationships between location of photo capture and photo privacy settings. Our data analysis leads to further questions which we investigate through a set of interviews with 15 users. The interviews reveal common themes in privacy considerations: security, social disclosure, identity and convenience. Finally, we highlight several implications and opportunities for design of media sharing applications, including using past privacy patterns to prevent oversights and errors.

Why do people tag? Users have mostly avoided annotating media such as photos -- both in desktop and mobile environments -- despite the many potential uses for annotations, including recall and retrieval. We investigate the incentives for annotation in Flickr, a popular web-based photo-sharing system, and ZoneTag, a cameraphone photo capture and annotation tool that uploads images to Flickr. In Flickr, annotation (as textual tags) serves both personal and social purposes, increasing incentives for tagging and resulting in a relatively high number of annotations. ZoneTag, in turn, makes it easier to tag cameraphone photos that are uploaded to Flickr by allowing annotation and suggesting relevant tags immediately after capture.

What happens when you can access all the world's media, but the access is constrained by screen size, bandwidth, attention, and battery life? We present a novel mobile context-aware software prototype that enables access to images on the go. Our prototype utilizes the channel metaphor to give users contextual access to media of interest according to key dimensions: spatial, social, and topical.

The advent of media-sharing sites like Flickr and YouTube has drastically increased the volume of community-contributed multimedia resources available on the web. These collections have a previously unimagined depth and breadth, and have generated new opportunities - and new challenges - to multimedia research. How do we analyze, understand and extract patterns from these new collections? How can we use these unstructured, unrestricted community contributions of media (and annotation) to generate "knowledge".

We describe an approach for extracting semantics of tags, unstructured text-labels assigned to resources on the Web, based on each tag's usage patterns. In particular, we focus on the problem of extracting place and event semantics for tags that are assigned to photos on Flickr, a popular photo sharing website that supports time and location (latitude/longitude) metadata. We analyze two methods inspired by well-known burst-analysis techniques and one novel method: Scale-structure Identification. We evaluate the methods on a subset of Flickr data, and show that our Scale-structure Identification method outperforms the existing techniques. The approach and methods described in this work can be used in other domains such as geo-annotated web pages, where text terms can be extracted and associated with usage patterns.

The availability of map interfaces and location-aware devices makes a growing amount of unstructured, geo-referenced information available on the Web. In particular, over twelve million geo-referenced photos are now available on Flickr, a popular photo-sharing website. We show a method to analyze the Flickr data and generate aggregate knowledge in the form of "representative tags" for arbitrary areas in the world. We display these tags on a map interface in an interactive web application along with images associated with each tag. We then use the implicit feedback of the aggregate user interactions with the tags and images to learn which images best describe the area shown on the map.

We address the problem of extracting semantics of tags -- short, unstructured text-labels assigned to resources on the Web -- based on each tag's metadata patterns. In particular, we describe an approach for extracting place and event semantics for tags that are assigned to photos on Flickr, a popular photo sharing website supporting time and location (latitude/longitude) metadata. The approach can be generalized to other domains where text terms can be extracted and associated with metadata patterns, such as geo-annotated web pages.

We describe a framework for automatically selecting a summary set of photographs from a large collection of geo-referenced photos. The summary algorithm is based on spatial patterns in photo sets, but can be expanded to support social, temporal, as well as textual-topical factors of the photo set. The summary set can be biased by the user, the content of the user's query, and the context in which the query is made. An initial evaluation on a set of geo-referenced photos shows that our algorithm performs well, producing results that are highly rated by users.

Given time and location information about digital photographs we can automatically generate an abundance of related contextual metadata, using off-the-shelf and Web-based data sources. Among these are the local daylight status and weather conditions at the time and place a photo was taken. This metadata has the potential of serving as memory cues and filters when browsing photo collections, especially as these collections grow into the tens of thousands and span dozens of years.