Non-consensual intimate imagery (NCII), also known as image-based sexual abuse (IBSA), is mediated through online platforms. Victim-survivors must turn to platforms to collect evidence and request content removal. Platforms act as the crime scene, judge, and jury, determining whether perpetrators face consequences and if harmful material is removed. We present a study of NCII victim-survivors' online reporting experiences, drawing on trauma-informed interviews with 13 participants. We find that platform reporting processes are hostile, opaque, and ineffective, often forcing complex harms into narrow interfaces, responding inconsistently, and failing to result in meaningful action. Leveraging institutional betrayal theory, we show how platforms' structures and practices compound harm, and, in doing so, surface concrete intervention points for redesigning reporting systems and shaping policy to better support victim-survivors

Peer Reviewed

Peer Reviewed

We introduce the Deep Value Benchmark (DVB), an evaluation framework that directly tests whether large language models (LLMs) learn fundamental human values or merely surface-level preferences. This distinction is critical for AI alignment: Systems that capture deeper values are likely to generalize human intentions robustly, while those that capture only superficial patterns in preference data risk producing misaligned behavior. The DVB uses a novel experimental design with controlled confounding between deep values (e.g., moral principles) and shallow features (e.g., superficial attributes). In the training phase, we expose LLMs to human preference data with deliberately correlated deep and shallow features---for instance, where a user consistently prefers (non-maleficence, formal language) options over (justice, informal language) alternatives. The testing phase then breaks these correlations, presenting choices between (justice, formal language) and (non-maleficence, informal language) options. This design allows us to precisely measure a model's Deep Value Generalization Rate (DVGR)---the probability of generalizing based on the underlying value rather than the shallow feature. Across 9 different models, the average DVGR is just 0.30. All models generalize deep values less than chance. Larger models have a (slightly) lower DVGR than smaller models. We are releasing our dataset, which was subject to three separate human validation experiments. DVB provides an interpretable measure of a core feature of alignment.

How do Wikipedians maintain an accurate encyclopedia during an ongoing geopolitical conflict where state actors might seek to spread disinformation or conduct an information operation? In the context of the Russia-Ukraine War, this question becomes more pressing, given the Russian government's extensive history of orchestrating information campaigns. We conducted an interview study with 13 expert Wikipedians involved in the Russo-Ukrainian War topic area on the English-language edition of Wikipedia. While our participants did not perceive there to be clear evidence of a state-backed information operation, they agreed that war-related articles experienced high levels of disruptive editing from both Russia-aligned and Ukraine-aligned accounts. The English-language edition of Wikipedia had existing policies and processes at its disposal to counter such disruption. State-backed or not, the disruptive activity created time-intensive maintenance work for our participants. Finally, participants considered English-language Wikipedia to be more resilient than social media in preventing the spread of false information online. We conclude by discussing sociotechnical implications for Wikipedia and social platforms.

Non-consensual Intimate Media (NCIM) refers to the distribution of sexual or intimate content without consent. NCIM is common and causes significant emotional, financial, and reputational harm. We developed Hands-Off, an interaction technique for messaging applications that deters non-consensual screenshots. Hands-Off requires recipients to perform a hand gesture in the air, above the device, to unlock media—which makes simultaneous screenshotting difficult. A lab study shows that Hands-Off gestures are easy to perform and reduce non-consensual screenshots by 67%. We conclude by generalizing this approach and introduce the idea of Feminist Interaction Techniques (FIT), interaction techniques that encode feminist values and speak to societal problems, and reflect on FIT’s opportunities and limitations.

Many studies explore how people "come into" misinformation exposure. But much less is known about how people "come out of" misinformation exposure. Do people organically sever ties to misinformation spreaders? And what predicts doing so? Over six months, we tracked the frequency and predictors of ~900K followers unfollowing ~5K health misinformation spreaders on Twitter. We found that misinformation ties are persistent. Monthly unfollowing rates are just 0.52%. In other words, 99.5% of misinformation ties persist each month. Users are also 31% more likely to unfollownon- misinformation spreaders than they are to unfollow misinformation spreaders. Although generally infrequent, the factors most associated with unfollowing misinformation spreaders are (1) redundancy and (2) ideology. First, users initially following many spreaders, or who follow spreaders that tweet often, are most likely to unfollow later. Second, liberals are more likely to unfollow than conservatives. Overall, we observe a strong persistence of misinformation ties. The fact that users rarely unfollow misinformation spreaders suggests a need for external nudges and the importance of preventing exposure from arising in the first place.

While interpretability tools are intended to help people better understand machine learning (ML), we find that they can, in fact, impair understanding. This paper presents a pre-registered, controlled experiment showing that ML practitioners (N=119) spent 5x less time on task, and were 17% less accurate about the data and model, when given access to interpretability tools. We present bounded rationality as the theoretical reason behind these findings. Bounded rationality presumes human departures from perfect rationality, and it is often effectuated by satisficing, i.e., an inclination towards "good enough" understanding. Adding interactive elements---a strategy often employed to promote deliberative thinking and engagement, and tested in our experiment---also does not help. We discuss implications for interpretability designers and researchers related to how cognitive and contextual factors can affect the effectiveness of interpretability tool use.

Non-consensual intimate media (NCIM) involves sharing intimate content without the depicted person's consent, including 'revenge porn' and sexually explicit deepfakes. While NCIM has received attention in legal, psychological, and communication fields over the past decade, it is not sufficiently addressed in computing scholarship. This paper addresses this gap by linking NCIM harms to the specific technological components that facilitate them. We introduce the sociotechnical stack , a conceptual framework designed to map the technical stack to its corresponding social impacts. The sociotechnical stack allows us to analyze sociotechnical problems like NCIM, and points toward opportunities for computing research. We propose a research roadmap for computing and social computing communities to deter NCIM perpetration and support victim-survivors through building and rebuilding technologies.

Social media platforms aspire to create online experiences where users can participate safely and equitably. However, women around the world experience widespread online harassment, including insults, stalking, aggression, threats, and non-consensual sharing of sexual photos. This article describes women's perceptions of harm associated with online harassment and preferred platform responses to that harm. We conducted a survey in 14 geographic regions around the world (N = 3,993), focusing on regions whose perspectives have been insufficiently elevated in social media governance decisions (e.g. Mongolia, Cameroon). Results show that, on average, women perceive greater harm associated with online harassment than men, especially for non-consensual image sharing. Women also prefer most platform responses compared to men, especially removing content and banning users; however, women are less favorable towards payment as a response. Addressing global gender-based violence online requires understanding how women experience online harms and how they wish for it to be addressed. This is especially important given that the people who build and govern technology are not typically those who are most likely to experience online harms.

Should social media platforms override a community’s self-policing when it repeatedly break rules? What actions can they consider? In light of this debate, platforms have begun experimenting with softer alternatives to outright bans. We examine one such intervention called quarantining, that impedes direct access to and promotion of controversial communities. Specifically, we present two case studies of what happened when Reddit quarantined the influential communities r/TheRedPill (TRP) and r/The_Donald (TD). Using over 85M Reddit posts, we apply causal inference methods to examine the quarantine’s effects on TRP and TD. We find that the quarantine made it more difficult to recruit new members: new user influx to TRP and TD decreased by 79.5% and 58%, respectively. Despite quarantining, existing users’ misogyny and racism levels remained unaffected. We conclude by reflecting on the effectiveness of this design friction in limiting the influence of toxic communities and discuss broader implications for content moderation.

Affirmative consent is the idea that someone must ask for, and earn, enthusiastic approval before interacting with someone else. For decades, feminist activists and scholars have used affirmative consent to theorize and prevent sexual assault. In this paper, we ask: Can affirmative consent help to theorize online interaction? Drawing from feminist, legal, and HCI literature, we introduce the feminist theory of affirmative consent and use it to analyze social computing systems. We present affirmative consent’s five core concepts: it is voluntary, informed, revertible, specific, and unburdensome. Using these principles, this paper argues that affirmative consent is both an explanatory and generative theoretical framework. First, affirmative consent is a theoretical abstraction for explaining various problematic phenomena in social platforms—including mass online harassment, revenge porn, and problems with content feeds. Finally, we argue that affirmative consent is a generative theoretical foundation from which to imagine new design ideas for consentful socio-technical systems.

Social signals are crucial when we decide if we want to interact with someone online. However, social signals are typically limited to the few that platform designers provide, and most can be easily manipulated. In this paper, we propose a new idea called synthesized social signals (S3s): social signals computationally derived from an account's history, and then rendered into the profile. Unlike conventional social signals such as profile bios, S3s use computational summarization to reduce receiver costs and raise the cost of faking signals. To demonstrate and explore the concept, we built Sig, an extensible Chrome extension that computes and visualizes S3s. After a formative study, we conducted a field deployment of Sig on Twitter, targeting two well-known problems on social media: toxic accounts and misinformation. Results show that Sig reduced receiver costs, added important signals beyond conventionally available ones, and that a few users felt safer using Twitter as a result. We conclude by reflecting on the opportunities and challenges S3s provide for augmenting interaction on social platforms.

Algorithms exert great power in curating online information, yet are often opaque in their operation, and even existence. Since opaque algorithms sometimes make biased or deceptive decisions, many have called for increased transparency. However, little is known about how users perceive and interact with potentially biased and deceptive opaque algorithms. What factors are associated with these perceptions, and how does adding transparency into algorithmic systems change user attitudes? To address these questions, we conducted two studies: 1) an analysis of 242 users' online discussions about the Yelp review filtering algorithm and 2) an interview study with 15 Yelp users disclosing the algorithm's existence via a tool. We found that users question or defend this algorithm and its opacity depending on their engagement with and personal gain from the algorithm. We also found adding transparency into the algorithm changed users' attitudes towards the algorithm: users reported their intention to either write for the algorithm in future reviews or leave the platform.

In this paper, we introduce a novel sociotechnical moderation system for Reddit called Crossmod. Through formative interviews with 11 active moderators from 10 different subreddits, we learned about the limitations of currently available automated tools, and how a new system could extend their capabilities. Developed out of these interviews, Crossmod makes its decisions based on cross-community learning---an approach that leverages a large corpus of previous moderator decisions via an ensemble of classifiers. Finally, we deployed Crossmod in a controlled environment, simulating real-time conversations from two large subreddits with over 10M subscribers each. To evaluate Crossmod's moderation recommendations, 4 moderators reviewed comments scored by Crossmod that had been drawn randomly from existing threads. Crossmod achieved an overall accuracy of 86% when detecting comments that would be removed by moderators, with high recall (over 87.5%). Additionally, moderators reported that they would have removed 95.3% of the comments flagged by Crossmod; however, 98.3% of these comments were still online at the time of this writing (i.e., not removed by the current moderation system). To the best of our knowledge, Crossmod is the first open source, AI-backed sociotechnical moderation system to be designed using participatory methods.

Thousands of users post on Reddit every day, but a fifth of all posts are removed. How do users react to these removals? We conducted a survey of 907 Reddit users, asking them to reflect on their post removal a few hours after it happened. Examining the qualitative and quantitative responses from this survey, we present users' perceptions of the platform's moderation processes. We find that although roughly a fifth (18%) of the participants accepted that their post removal was appropriate, a majority of the participants did not --- over a third (37%) of the participants did not understand why their post was removed, and further, 29% of the participants expressed some level of frustration about the removal. We focus on factors that shape users' attitudes aboutfairness in moderation andposting again in the community. Our results indicate that users who read community guidelines or receive explanations for removal are more likely to perceive the removal as fair and post again in the future. We discuss implications for moderation practices and policies. Our findings suggest that the extra effort required to establish community guidelines and educate users with helpful feedback is worthwhile, leading to better user attitudes about fairness and propensity to post again.

When posts are removed on a social media platform, users may or may not receive an explanation. What kinds of explanations are provided? Do those explanations matter? Using a sample of 32 million Reddit posts, we characterize the removal explanations that are provided to Redditors, and link them to measures of subsequent user behaviors---including future post submissions and future post removals. Adopting a topic modeling approach, we show that removal explanations often provide information that educate users about the social norms of the community, thereby (theoretically) preparing them to become a productive member. We build regression models that show evidence of removal explanations playing a role in future user activity. Most importantly, we show that offering explanations for content moderation reduces the odds of future post removals. Additionally, explanations provided by human moderators did not have a significant advantage over explanations provided by bots for reducing future post removals. We propose design solutions that can promote the efficient use of explanation mechanisms, reflecting on how automated moderation tools can contribute to this space. Overall, our findings suggest that removal explanations may be under-utilized in moderation practices, and it is potentially worthwhile for community managers to invest time and resources into providing them.

What one may say on the internet is increasingly controlled by a mix of automated programs, and decisions made by paid and volunteer human moderators. On the popular social media site Reddit, moderators heavily rely on a configurable, automated program called “Automoderator” (or “Automod”). How do moderators use Automod? What advantages and challenges does the use of Automod present? We participated as Reddit moderators for over a year, and conducted interviews with 16 moderators to understand the use of Automod in the context of the sociotechnical system of Reddit. Our findings suggest a need for audit tools to help tune the performance of automated mechanisms, a repository for sharing tools, and improving the division of labor between human and machine decision making. We offer insights that are relevant to multiple stakeholders—creators of platforms, designers of automated regulation systems, scholars of platform governance, and content moderators.

Since 2014, Venezuela has experienced severe economic crisis, including scarcity of basic necessities such as food and medicine. This has resulted in over-priced goods, scams, and other forms of economic abuse. We present an investigation of Venezuelans' efforts to form an alternative, Solidarity Economy (SE) through Facebook Groups. In these groups, individuals can barter for items at fair prices. We highlight group practices and design features of Facebook Groups which support solidarity or anti-solidarity behaviors. We conclude by leveraging design principles for online communities presented by Kollock to present strategies to design more effective SEs in environments of low trust.

Norms are central to how online communities are governed. Yet, norms are also emergent, arise from interaction, and can vary significantly between communities---making them challenging to study at scale. In this paper, we study community norms on Reddit in a large-scale, empirical manner. Via 2.8M comments removed by moderators of 100 top subreddits over 10 months, we use both computational and qualitative methods to identify three types of norms: macro norms that are universal to most parts of Reddit; meso norms that are shared across certain groups of subreddits; and micro norms that are specific to individual, relatively unique subreddits. Given the size of Reddit's user base---and the wide range of topics covered by different subreddits---we argue this represents the first large-scale census of the norms in broader internet culture. In other words, these findings shed light on what Reddit values, and how widely-held those values are. We conclude by discussing implications for the design of new and existing online communities.

Online harassment is a complex and growing problem. On Twitter, one mechanism people use to avoid harassment is the blocklist , a list of accounts that are preemptively blocked from interacting with a subscriber. In this article, we present a rich description of Twitter blocklists – why they are needed, how they work, and their strengths and weaknesses in practice. Next, we use blocklists to interrogate online harassment – the forms it takes, as well as tactics used by harassers. Specifically, we interviewed both people who use blocklists to protect themselves, and people who are blocked by blocklists. We find that users are not adequately protected from harassment, and at the same time, many people feel that they are blocked unnecessarily and unfairly. Moreover, we find that not all users agree on what constitutes harassment. Based on our findings, we propose design interventions for social network sites with the aim of protecting people from harassment, while preserving freedom of speech.

Since its earliest days, harassment and abuse have plagued the Internet. Recent research has focused on in-domain methods to detect abusive content and faces several challenges, most notably the need to obtain large training corpora. In this paper, we introduce a novel computational approach to address this problem called Bag of Communities (BoC)---a technique that leverages large-scale, preexisting data from other Internet communities. We then apply BoC toward identifying abusive behavior within a major Internet community. Specifically, we compute a post's similarity to 9 other communities from 4chan, Reddit, Voat and MetaFilter. We show that a BoC model can be used on communities "off the shelf" with roughly 75% accuracy---no training examples are needed from the target community. A dynamic BoC model achieves 91.18% accuracy after seeing 100,000 human-moderated posts, and uniformly outperforms in-domain methods. Using this conceptual and empirical work, we argue that the BoC approach may allow communities to deal with a range of common problems, like abusive behavior, faster and with fewer engineering resources.

When social networking sites give users granular control over their privacy settings, the result is that some content across the site is public and some is not. How might this content--or characteristics of users who post publicly versus to a limited audience--be different? If these differences exist, research studies of public content could potentially be introducing systematic bias. Via Mechanical Turk, we asked 1,815 Facebook users to share recent posts. Using qualitative coding and quantitative measures, we characterize and categorize the nature of the content. Using machine learning techniques, we analyze patterns of choices for privacy settings. Contrary to expectations, we find that content type is not a significant predictor of privacy setting; however, some demographics such as gender and age are predictive. Additionally, with consent of participants, we provide a dataset of nearly 9,000 public and non-public Facebook posts.

Social media has increasingly become central to the way billions of people experience news and events, often bypassing journalists---the traditional gatekeepers of breaking news. Naturally, this casts doubt on the credibility of information found on social media. Here we ask: Can the language captured in unfolding Twitter events provide information about the event's credibility? By examining the first large-scale, systematically-tracked credibility corpus of public Twitter messages (66M messages corresponding to 1,377 real-world events over a span of three months), and identifying 15 theoretically grounded linguistic dimensions, we present a parsimonious model that maps language cues to perceived levels of credibility. While not deployable as a standalone model for credibility assessment at present, our results show that certain linguistic categories and their associated phrases are strong predictors surrounding disparate social media events. In other words, the language used by millions of people on Twitter has considerable information about an event's credibility. For example, hedge words and positive emotion words are associated with lower credibility.

In 2015, Reddit closed several subreddits-foremost among them r/fatpeoplehate and r/CoonTown-due to violations of Reddit's anti-harassment policy. However, the effectiveness of banning as a moderation approach remains unclear: banning might diminish hateful behavior, or it may relocate such behavior to different parts of the site. We study the ban of r/fatpeoplehate and r/CoonTown in terms of its effect on both participating users and affected subreddits. Working from over 100M Reddit posts and comments, we generate hate speech lexicons to examine variations in hate speech usage via causal inference methods. We find that the ban worked for Reddit. More accounts than expected discontinued using the site; those that stayed drastically decreased their hate speech usage-by at least 80%. Though many subreddits saw an influx of r/fatpeoplehate and r/CoonTown "migrants," those subreddits saw no significant changes in hate speech usage. In other words, other subreddits did not inherit the problem. We conclude by reflecting on the apparent success of the ban, discussing implications for online moderation, Reddit and internet communities more broadly.

Today, social media provide the means by which billions of people experience news and events happening around the world. However, the absence of traditional journalistic gatekeeping allows information to flow unencumbered through these platforms, often raising questions of veracity and credibility of the reported information. Here we ask: How do the dynamics of collective attention directed toward an event reported on social media vary with its perceived credibility? By examining the first large-scale, systematically tracked credibility database of public Twitter messages (47M messages corresponding to 1,138 real-world events over a period of three months), we established a relationship between the temporal dynamics of events reported on social media and their associated level of credibility judgments. Representing collective attention by the aggregate temporal signatures of an event's reportage, we found that the amount of continued attention focused on an event provides information about its associated levels of perceived credibility. Events exhibiting sustained, intermittent bursts of attention were found to be associated with lower levels of perceived credibility. In other words, as more people showed interest during moments of transient collective attention, the associated uncertainty surrounding these events also increased.

Pro-eating disorder (pro-ED) communities on social media encourage the adoption and maintenance of disordered eating habits as acceptable alternative lifestyles rather than threats to health. In particular, the social networking site Instagram has reacted by banning searches on several pro-ED tags and issuing content advisories on others. We pre-sent the first large-scale quantitative study investigating pro-ED communities on Instagram in the aftermath of moderation -- our dataset contains 2.5M posts between 2011 and 2014. We find that the pro-ED community has adopted non-standard lexical variations of moderated tags to circumvent these restrictions. In fact, increasingly complex lexical variants have emerged over time. Communities that use lexical variants show increased participation and support of pro-ED (15-30%). Finally, the tags associated with content on these variants express more toxic, self-harm, and vulnerable content. Despite Instagram's moderation strategies, pro-ED communities are active and thriving. We discuss the effectiveness of content moderation as an intervention for communities of deviant behavior.

Recent news has made social media notorious for both abusing user data and allowing governments to scrutinize personal information. Nevertheless, people still enjoy connecting with friends and families through social media but fail to use it to connect to local communities where we live our daily lives. In this paper, we present Popup Networks, a new platform for building hyper-local social computing applications, running on home wireless routers via an underlying mesh network. Summative interviews illustrate interests in using Popup Networks to create new local ties and as a backup in the case of Internet disruption. By utilizing locality to ward off external risks, Popup Networks provide alternative privacy, visibility, and economic models compared to traditional social media. While deploying Popup Networks would be an ideal evaluation, we argue that the technical tests and user interviews we conducted are suitable for socially complex systems such as Popup Networks--advocating an agenda moving forward for social computing systems research.

Both governments and corporations routinely surveil computer-mediated communication (CMC). Technologists often suggest widespread encryption as a defense mechanism, but CMC encryption schemes have historically faced significant usability and adoption problems. Here, we introduce a novel technique called Open Book designed to address these two problems. Inspired by how people deal with eavesdroppers offline, Open Book uses data mining and natural language processing to transform CMC messages into ones that are vaguer than the original. Specifically, we present: 1) a greedy Open Book algorithm that cloaks messages by transforming them to resemble the average Internet message; 2) an open-source, browser-based instantiation of it called Read Me, designed for Gmail; and, 3) a set of experiments showing that intended recipients can decode Open Book messages, but that unintended human- and machine-recipients cannot. Finally, we reflect on some open questions raised by this approach, such as recognizability and future side-channel attacks.

We propose a technique we call piggyback prototyping, a prototyping mechanism for designing new social computing systems on top of existing ones. Traditional HCI prototyping techniques do not translate well to large social computing systems. To address this gap, we describe a 6-stage process for prototyping new social computing systems using existing online systems, such as Twitter or Facebook. This allows researchers to focus on what people do on their system rather than how to attract people to it. We illustrate this technique with an instantiation on Twitter to pair people who are different from each other in airports. Even though there were many missed meetings, 53% of survey respondents would be interested in being matched again, and eight people even met in person. Through piggyback prototyping, we gained insight into the future design of this system. We conclude the paper with considerations for privacy, consent, volume of users, and evaluation metrics.

Difficulty in navigating daily life can lead to frustration and decrease independence for people with autism. While they turn to online autism communities for information and advice for coping with everyday challenges, these communities may present only a limited perspective because of their in-group nature. Obtaining support from out-group sources beyond the in-group community may prove valuable in dealing with challenging situations such as public anxiety and workplace conflicts. In this paper, we explore the value of supplementary out-group support from crowdsourced responders added to in-group support from a community of members. We find that out-group sources provide relatively rapid, concise responses with direct and structured information, socially appropriate coping strategies without compromising emotional value. Using an autism community as a motivating example, we conclude by providing design implications for combining in-group and out-group resources that may enhance the question-and-answer experience.

In the past half-decade, Amazon Mechanical Turk has radically changed the way many scholars do research. The availability of a massive, distributed, anonymous crowd of individuals willing to perform general human-intelligence micro-tasks for micro-payments is a valuable resource for researchers and practitioners. This paper addresses the challenges of obtaining quality annotations for subjective judgment oriented tasks of varying difficulty. We design and conduct a large, controlled experiment (N=68,000) to measure the efficacy of selected strategies for obtaining high quality data annotations from non-experts. Our results point to the advantages of person-oriented strategies over process-oriented strategies. Specifically, we find that screening workers for requisite cognitive aptitudes and providing training in qualitative coding techniques is quite effective, significantly outperforming control and baseline conditions. Interestingly, such strategies can improve coder annotation accuracy above and beyond common benchmark strategies such as Bayesian Truth Serum (BTS).

We present the results of an 8-week pilot study with 55 dogs investigating whether using quantimetric monitors and a companion smartphone application can reduce returns and increase the perceived strength of bonds between newly adopted dogs from the Humane Society of Silicon Valley and their adopters. Through this pilot study, we developed guidelines for future research and discovered promising results indicating that providing dog quantimetric data to adopters through the use of a smartphone application could yield reduced rates of re-relinquishment. Additionally, respondents indicated that they felt using the smartphone application helped them to better meet the activity needs of their dog and increased the bond between themselves and their newly adopted dog.

Photos are becoming prominent means of communication online. Despite photos' pervasive presence in social media and online world, we know little about how people interact and engage with their content. Understanding how photo content might signify engagement, can impact both science and design, influencing production and distribution. One common type of photo content that is shared on social media, is the photos of people. From studies of offline behavior, we know that human faces are powerful channels of non-verbal communication. In this paper, we study this behavioral phenomena online. We ask how presence of a face, it's age and gender might impact social engagement on the photo. We use a corpus of 1 million Instagram images and organize our study around two social engagement feedback factors, likes and comments. Our results show that photos with faces are 38% more likely to receive likes and 32% more likely to receive comments, even after controlling for social network reach and activity. We find, however, that the number of faces, their age and gender do not have an effect. This work presents the first results on how photos with human faces relate to engagement on large scale image sharing communities. In addition to contributing to the research around online user behavior, our findings offer a new line of future work using visual analysis.

Consumer product reviews are the backbone of commerce online. Most commonly, sites ask users for their personal opinions on a product or service. I conjecture, however, that this traditional method of eliciting reviews often invites idiosyncratic viewpoints. In this paper, I present a statistical study examining the differences between traditionally elicited product ratings (i.e., "How do you rate this product'") and social inference ratings (i.e., "How do you think other people will rate this product'"). In 5 of 6 trials, I find that social inference ratings produce the same aggregate product rating as the one produced via traditionally elicited ratings. In all cases, however, social inferences yield less variance. This is significant because using social inference ratings 1) therefore converges on the true aggregate product rating faster, and 2) is a cheap design intervention on the part of existing sites.

The term email overload has two definitions: receiving a large volume of incoming email, and having emails of different status types (to do, to read, etc). Whittaker and Sidner proposed the latter definition in 1996, noticing that email inboxes were far more complex than simply containing incoming messages. Sixteen years after Whittaker and Sidner, we replicate and extend their work with a qualitative analysis of Google's Gmail. We find that email overload, both in terms of volume and of status, is still a problem today. Our contributions are 1) updating the state of email overload, 2) extending our understanding of overload in the context of Gmail and 3) comparing personal with work email accounts: while work email tends to be status overloaded, personal email is also type overloaded. These comparisons between work and personal email suggest new avenues for email research.

This paper presents a study of Nextdoor, a social media system designed to support local neighborhoods. While not the first system designed to support community engagement, Nextdoor has a number of attributes that make it distinct. Our study, across three communities in a major U.S. city, illustrates that Nextdoor inhabits an already-rich ecosystem of community-oriented social media, but is being appropriated by its users for use in different ways than these existing media. Nextdoor also raises tensions in how it defines the boundaries of neighborhoods, and in the privacy issues it raises among its users.

Pinterest is a popular social curation site where people collect, organize, and share pictures of items. We studied a fundamental issue for such sites: what patterns of activity attract attention (audience and content reposting)-- We organized our studies around two key factors: the extent to which users specialize in particular topics, and homophily among users. We also considered the existence of differences between female and male users. We found: (a) women and men differed in the types of content they collected and the degree to which they specialized; male Pinterest users were not particularly interested in stereotypically male topics; (b) sharing diverse types of content increases your following, but only up to a certain point; (c) homophily drives repinning: people repin content from other users who share their interests; homophily also affects following, but to a lesser extent. Our findings suggest strategies both for users (e.g., strategies to attract an audience) and maintainers (e.g., content recommendation methods) of social curation sites.

Most people associate with people like themselves, a process called homophily. Exposure to diversity, however, makes us more informed as individuals and as a society. In this paper, we investigate political disagreements on Facebook to explore the conditions under which diverse opinions can coexist online. Via a mixed methods approach comprising 103 survey responses and 13 interviews with politically engaged American social media users, we found that participants who perceived more differences with their friends engaged less on Facebook than those who perceived more homogeneity. Weak ties were particularly brittle to political disagreements, despite being the ties most likely to offer diversity. Finally, based on our findings we suggest potential design opportunities to bridge across ideological difference: 1) support exposure to weak ties; and 2) make common ground visible while friends converse.

To increase productivity, informal learning, and collaborations within and across research groups, we have been experimenting with a new kind of interaction that we call {em pair research}, in which members are paired up weekly to work together on each other's projects. In this paper, we present a system for making pairings and present results from two deployments. Results show that members used pair research in a wide variety of ways including pair programming, user testing, brainstorming, and data collection and analysis. Pair research helped members get things done and share their expertise with others.

Crowdfunding sites like Kickstarter--where entrepreneurs and artists look to the internet for funding--have quickly risen to prominence. However, we know very little about the factors driving the 'crowd' to take projects to their funding goal. In this paper we explore the factors which lead to successfully funding a crowdfunding project. We study a corpus of 45K crowdfunded projects, analyzing 9M phrases and 59 other variables commonly present on crowdfunding sites. The language used in the project has surprising predictive power accounting for 58.56% of the variance around successful funding. A closer look at the phrases shows they exhibit general persuasion principles. For example, also receive two reflects the principle of Reciprocity and is one of the top predictors of successful funding. We conclude this paper by announcing the release of the predictive phrases along with the control variables as a public dataset, hoping that our work can enable new features on crowdfunding sites--tools to help both backers and project creators make the best use of their time and money.

Online recommendation sites are valuable information sources that people contribute to, and often use to choose restaurants. However, little is known about the dynamics behind participation in these online communities and how the recommendations in these communities are formed. In this work, we take a first look at online restaurant recommendation communities to study what endogenous (i.e., related to entities being reviewed) and exogenous factors influence people's participation in the communities, and to what extent. We analyze an online community corpus of 840K restaurants and their 1.1M associated reviews from 2002 to 2011, spread across every U.S. state. We construct models for number of reviews and ratings by community members, based on several dimensions of endogenous and exogenous factors. We find that while endogenous factors such as restaurant attributes (e.g., meal, price, service) affect recommendations, surprisingly, exogenous factors such as demographics (e.g., neighborhood diversity, education) and weather (e.g., temperature, rain, snow, season) also exert a significant effect on reviews. We find that many of the effects in online communities can be explained using offline theories from experimental psychology. Our study is the first to look at exogenous factors and how it related to online online restaurant reviews. It has implications for designing online recommendation sites, and in general, social media and online communities.

In this paper we propose a new methodology for gaining insight into the temporal aspects of social networks. In order to develop higher-level, large-scale data analysis methods for classification, prediction, and anomaly detection, a solid foundation of analytical techniques is required. We present a novel approach to the analysis of these networks that leverages time series and statistical techniques to quantitatively describe the temporal nature of a social network. We report on the application of our approach toward a real data set and successfully visualize high-level changes to the network as well as discover outlying vertices.

Over the past decade, social network sites have become ubiquitous places for people to maintain relationships, as well as loci of intense research interest. Recently, a new site has exploded into prominence: Pinterest became the fastest social network to reach 10M users, growing 4000% in 2011 alone. While many Pinterest articles have appeared in the popular press, there has been little scholarly work so far. In this paper, we use a quantitative approach to study three research questions about the site. What drives activity on Pinterest? What role does gender play in the site's social connections? And finally, what distinguishes Pinterest from existing networks, in particular Twitter? In short, we find that being female means more repins, but fewer followers, and that four verbs set Pinterest apart from Twitter: use, look, want and need. This work serves as an early snapshot of Pinterest that later work can leverage.

Follower count is important to Twitter users: it can indicate popularity and prestige. Yet, holistically, little is understood about what factors -- like social behavior, message content, and network structure - lead to more followers. Such information could help technologists design and build tools that help users grow their audiences. In this paper, we study 507 Twitter users and a half-million of their tweets over 15 months. Marrying a longitudinal approach with a negative binomial auto-regression model, we find that variables for message content, social behavior, and network structure should be given equal consideration when predicting link formations on Twitter. To our knowledge, this is the first longitudinal study of follow predictors, and the first to show that the relative contributions of social behavior and mes-sage content are just as impactful as factors related to social network structure for predicting growth of online social networks. We conclude with practical and theoretical implications for designing social media technologies.

Many online communities ask their members to do work for the good of everyone on the site. On social voting sites like Reddit, this means that users judge a stream of incoming links by voting them up or down. The links with the most up-votes bubble up to the main page, pointing everyone toward the best content. A threat to all sites designed this way, however, is underprovision: when too many people rely on others to contribute without doing so themselves. In this paper, we present findings suggesting that widespread underprovision of votes is happening on Reddit, arguably the internet's largest social voting community. Notably, Reddit overlooked 52% of the most popular links the first time they were submitted. This suggests that many potentially popular links get ignored, jeopardizing the site's core purpose. We conclude by discussing possible reasons behind it, and suggest future research on social voting sites.

Social translucence is a landmark theory in social computing. Modeled on physical life, it guides designers toward elegant social technologies. However, we argue that it breaks down over modern social network sites because social networks resist its physical metaphors. In this paper, we build theory relating social translucence to social network structure. To explore this idea, we built a tool called Link Different. Link Different addresses a structural awareness problem by letting users know how many of their Twitter followers already a saw link via someone else they follow. During two months on the web, nearly 150K people used the site a total of 1.3M times. Its widespread, viral use suggests that people want social translucence, but network structure gets in the way. We conclude the paper by illustrating new design problems that lie at the intersection of social translucence and other unexplored network structures.

Hierarchy fundamentally shapes how we act at work. In this paper, we explore the relationship between the words people write in workplace email and the rank of the email's recipient. Using the Enron corpus as a dataset, we perform a close study of the words and phrases people send to those above them in the corporate hierarchy versus those at the same level or lower. We find that certain words and phrases are strong predictors. For example, "thought you would" strongly suggests that the recipient outranks the sender, while "let's discuss" implies the opposite. We also find that the phrases people write to their bosses do not demonstrate cognitive processes as often as the ones they write to others. We conclude this paper by interpreting our results and announcing the release of the predictive phrases as a public dataset, perhaps enabling a new class of status-aware applications.

We have friends we consider very close and acquaintances we barely know. The social sciences use the term tie strength to denote this differential closeness with the people in our lives. In this paper, we explore how well a tie strength model developed for one social medium adapts to another. Specifically, we present a Twitter application called We Meddle which puts a Facebook tie strength model at the core of its design. We Meddle estimated tie strengths for more than 200,000 online relationships from people in 52 countries. We focus on the mapping of Facebook relational features to relational features in Twitter. By examining We Meddle's mistakes, we find that the Facebook tie strength model largely generalizes to Twitter. This is early evidence that important relational properties may manifest similarly across different social media, a finding that would allow new social media sites to build around relational findings from old ones.

People who review products on the web invest considerable time and energy in what they write. So why would someone write a review that restates earlier reviews? Our work looks to answer this question. In this paper, we present a mixed-method study of deja reviewers, latecomers who echo what other people said. We analyze nearly 100,000 Amazon.com reviews for signs of repetition and find that roughly 10-15% of reviews substantially resemble previous ones. Using these algorithmically-identified reviews as centerpieces for discussion, we interviewed reviewers to understand their motives. An overwhelming number of reviews partially explains deja reviews, but deeper factors revolving around an individual's status in the community are also at work. The paper concludes by introducing a new idea inspired by our findings: a self-aware community that nudges members toward community-wide goals.

Social media treats all users the same: trusted friend or total stranger, with little or nothing in between. In reality, relationships fall everywhere along this spectrum, a topic social science has investigated for decades under the theme of tie strength. Our work bridges this gap between theory and practice. In this paper, we present a predictive model that maps social media data to tie strength. The model builds on a dataset of over 2,000 social media ties and performs quite well, distinguishing between strong and weak ties with over 85% accuracy. We complement these quantitative findings with interviews that unpack the relationships we could not predict. The paper concludes by illustrating how modeling tie strength can improve social media design elements, including privacy controls, message routing, friend introductions and information prioritization.

In this paper we argue that social visualization can motivate contributors to social production projects, such as Wikipedia and open source development. As evidence, we present CodeSaw, a social visualization of open source software development that we studied with real open source communities. CodeSaw mines open source archives to visualize group dynamics that currently lie buried in textual databases. Furthermore, CodeSaw becomes an active social space itself by supporting comments directly inside the visualization. To demonstrate CodeSaw, we apply it to a popular open source project, showing how the visualization reveals group dynamics and individual roles. The paper concludes by presenting evidence that CodeSaw, and social visualization more generally, can motivate contributors to social production projects if the visualization leaves the laboratory and makes it to the community visualized.

History repeatedly demonstrates that rural communities have unique technological needs. Yet, we know little about how rural communities use modern technologies, so we lack knowledge on how to design for them. To address this gap, our empirical paper investigates behavioral differences between more than 3,000 rural and urban social media users. Using a dataset collected from a broadly popular social network site, we analyze users' profiles, 340,000 online friendships and 200,000 interpersonal messages. Using social capital theory, we predict differences between rural and urban users and find strong evidence supporting our hypotheses. Namely, rural people articulate far fewer friends online, and those friends live much closer to home. Our results also indicate that the groups have substantially different gender distributions and use privacy features differently. We conclude by discussing design implications drawn from our findings; most importantly, designers should reconsider the binary friend-or-not model to allow for incremental trust-building.

Abstract The GriPhyN virtual data system provides a suite of components and services for data‐intensive sciences that enables scientists to systematically and efficiently describe, discover, and share large‐scale data and computational resources. We describe the design and implementation of such middleware services in terms of a virtual data system interface called Chiron, and present virtual data integration examples from the QuarkNet education project and from functional‐MRI‐based neuroscience research. The Chiron interface also serves as an online ‘educator’ for virtual data applications. Copyright © 2005 John Wiley & Sons, Ltd.

We describe a case study that uses grid computing techniques to support the collaborative learning of high school students investigating cosmic rays. Students gather and upload science data to our e-Lab portal They explore those data using techniques from the GriPhyN collaboration. These techniques include virtual data transformations, workflows, metadata cataloging and indexing, data product provenance and persistence, as well as job planners. Students use Web browsers and a custom interface that extends the GriPhyN Chiron portal to perform all of these tasks. They share results in the form of online posters and ask each other questions in this asynchronous environment. Students can discover and extend the research of other students, modeling the processes of modern large-scale scientific collaborations. Also, the e-Lab portal (http://quarknet.uchicago.edu/elab/cosmic) provides tools for teachers to guide student work throughout an investigation.