Video-sharing platforms (VSPs) have become central information hubs but also facilitate the spread of information disorder, from misleading narratives to fabricated content. This survey synthesizes research on VSPs' multimedia ecosystems across three dimensions: (1) types of information disorder, (2) methodological approaches, and (3) platform features. We conclude by identifying key challenges and open questions for future research.

Large Language Models (LLMs) have created opportunities for designing chatbots that can support complex question-answering (QA) scenarios and improve news audience engagement. However, we still lack an understanding of what roles journalists and readers deem fit for such a chatbot in newsrooms. To address this gap, we first interviewed six journalists to understand how they answer questions from readers currently and how they want to use a QA chatbot for this purpose. To understand how readers want to interact with a QA chatbot, we then conducted an online experiment (N=124) where we asked each participant to read three news articles and ask questions to either the author(s) of the articles or a chatbot. By combining results from the studies, we present alignments and discrepancies between how journalists and readers want to use QA chatbots and propose a framework for designing effective QA chatbots in newsrooms.

Polarization and the marketplace for impressions have conspired to make navigating information online difficult for users, and while there has been a significant effort to detect false or misleading text, multimodal datasets have received considerably less attention.To complement existing resources, we present multimodal Video Misleading Headline (VMH), a dataset that consists of videos and whether annotators believe the headline is representative of the video's contents.After collecting and annotating this dataset, we analyze multimodal baselines for detecting misleading headlines.Our annotation process also focuses on why annotators view a video as misleading, allowing us to better understand the interplay of annotators' background and the content of the videos.

Framing analysis is predominantly qualitative and quantitative, examining a small dataset with manual coding.Easy access to digital data in the last two decades prompts scholars in both computation and social sciences to utilize various computational methods to explore frames in large-scale datasets.The growing scholarship, however, lacks a comprehensive understanding and resources of computational framing analysis methods.Aiming to address the gap, this article surveys existing computational framing analysis approaches and puts them together.The research is expected to help scholars and journalists gain a deeper understanding of how frames are being explored computationally, better equip them to analyze frames in large-scale datasets, and, finally, work on advancing methodological approaches.

The factors influencing people’s food decisions, such as one’s mood and eating environment, are important information to foster self-reflection and to develop personalized healthy diet. But, it is difficult to consistently collect them due to the heavy data capture burden. In this work, we examine how speech input supports capturing everyday food practice through a week-long data collection study (N = 11). We deployed FoodScrap, a speech-based food journaling app that allows people to capture food components, preparation methods, and food decisions. Using speech input, participants detailed their meal ingredients and elaborated their food decisions by describing the eating moments, explaining their eating strategy, and assessing their food practice. Participants recognized that speech input facilitated self-reflection, but expressed concerns around re-recording, mental load, social constraints, and privacy. We discuss how speech input can support low-burden and reflective food journaling and opportunities for effectively processing and presenting large amounts of speech data.

Studies show that users do not reliably click more often on headlines classified as clickbait by automated classifiers. Is this because the linguistic criteria (e.g., use of lists or questions) emphasized by the classifiers are not psychologically relevant in attracting interest, or because their classifications are confounded by other unknown factors associated with assumptions of the classifiers? We address these possibilities with three studies—a quasi-experiment using headlines classified as clickbait by three machine-learning models (Study 1), a controlled experiment varying the headline of an identical news story to contain only one clickbait characteristic (Study 2), and a computational analysis of four classifiers using real-world sharing data (Study 3). Studies 1 and 2 revealed that clickbait did not generate more curiosity than non-clickbait. Study 3 revealed that while some headlines generate more engagement, the detectors agreed on a classification only 47% of the time, raising fundamental questions about their validity.

Most scholarly discussions around ridesharing applications center on the experiences of the drivers and the riders (passengers), and thus the role of the owners of the cars, if they are different from the drivers, remain understudied. However, in many countries in the Global South, the car owners are often different from the car drivers, and the tensions between them often shape the experience with these ridesharing apps in those countries. In this paper, we address this issue based on our interview-based study in Dhaka, Bangladesh, which incorporates semi-structured interviews of 31 Uber drivers and 10 car owners. From our interviews, we identify the contract models that facilitate the partnership between prospective Uber drivers without a car and car owners seeking to rent their cars for Uber, describe the tensions between these two parties, provide a nuanced cultural portrayal of their negotiation mechanisms, and highlight the reasons for which the driver or the owner leaves Uber. Our analysis reveals how the local adoption of sharing economy amplifies existing inequalities and disrupts the prevailing social dynamics. We also connect our findings to the broader interests of CSCW around work, privacy, power and discuss their implications for design and policy formulations.

Today Information in the world wide web is overwhelmed by unprecedented quantity of data on versatile topics with varied quality. However, the quality of information disseminated in the field of medicine has been questioned as the negative health consequences of health misinformation can be life-threatening. There is currently no generic automated tool for evaluating the quality of online health information spanned over broad range. To address this gap, in this paper, we applied data mining approach to automatically assess the quality of online health articles based on 10 quality criteria. We have prepared a labelled dataset with 53012 features and applied different feature selection methods to identify the best feature subset with which our trained classifier achieved an accuracy of [Formula: see text] varied over 10 criteria. Our semantic analysis of features shows the underpinning associations between the selected features & assessment criteria and further rationalize our assessment approach. Our findings will help in identifying high quality health articles and thus aiding users in shaping their opinion to make right choice while picking health related help from online.

There has been a growing interest within CSCW community in understanding the characteristics of misinformation propagated through computational media, and the devising techniques to address the associated challenges. However, most work in this area has been concentrated on the cases in the western world leaving a major portion of this problem unaddressed that is situated in the Global South. This paper aims to broaden the scope of this discourse by focusing on this problem in the context of Bangladesh, a country in the Global South. The spread of misinformation on Facebook in Bangladesh, a country with a population of over 163 million, has resulted in chaos, hate attacks, and killings. By interviewing journalists, fact-checkers, in addition to surveying the general public, we analyzed the current state of verifying misinformation in Bangladesh. Our findings show that most people in the 'news audience' want the news media to verify the authenticity of online information that they see online. However, the newspaper journalists say that fact-checking online information is not a part of their job, and it is also beyond their capacity given the amount of information being published online every day. We further find that the voluntary fact-checkers in Bangladesh are not equipped with sufficient infrastructural support to fill in this gap. We show how our findings are connected to some of the core concerns of CSCW community around social media, collaboration, infrastructural politics, and information inequality. From our analysis, we also suggest several pathways to increase the impact of fact-checking efforts through collaboration, technology design, and infrastructure development.

A series of events in October 2017 led to the initiation of an unprecedented global feminist movement over various social media platforms, where using the hashtag #MeToo (or some variants of it), women across the world publicly shared their untold stories of being sexually harassed. We conducted an anonymous online survey (n=180) and an interview study (n=30) to understand the participation of Bangladeshi women in this movement. Our study concurs that while Bangladeshi women, who are regular users of social media, supported the spirit of this movement; did not participate in it, even though they had many bitter experiences. Our analysis shows that their non-participation was largely influenced by a cultural difference, patriarchy, perceived futility and lack of hope, and a reliance on alternatives. We discuss how our findings of women's use of technology platforms, which is conditioned and limited by male-dominated and conservative Bangladeshi society, relates to the broader issues in feminism that the GROUP community is interested in.

Sexual violence is a serious problem across the globe. A lot of victims, particularly women, go through this experience. Unfortunately, not all of these violent incidents come to public. A large portion of victims don't disclose their experience. On the September of 2018, people started revealing in Twitter why they didn't report a sexual violence experience using a hashtag #WhyIDidntReport. We collect about 40K such tweets and conduct a large-scale supervised analysis of why victims don't report. Our study finds the extent to which people shared their reasons as well as categorizes the reasons into finer reasons. We also analyze user engaged with the victims and compare our findings with existing literature.

In October 2017, there happened the uprising of an unprecedented online movement on social media by women across the world who started publicly sharing their untold stories of being sexually harassed along with the hashtag #MeToo (or some variants of it). Those stories did not only strike the silence that had long hid the perpetrators, but also allowed women to discharge some of their bottled-up grievances, and revealed many important information surrounding sexual harassment. In this paper, we present our analysis of about one million such tweets collected between October 15 and October 31, 2017 that reveals some interesting patterns and attributes of the people, place, emotions, actions, and reactions related to the tweeted stories. Based on our analysis, we also advance the discussion on the potential role of online social media in breaking the silence of women by factoring in the strengths and limitations of these platforms.

In October 2017, an unprecedented social media movement started where women from all around the world publicly shared their untold stories of being sexually harassed using the hashtag #MeToo (or some variants of it). While this movement raised voices against sexual harassment across the globe, many women in South Asia did not participate in this movement. In this paper, we present our study on non-participation of many Bangladeshi women in this movement through an anonymous online survey (n = 180), and an in-depth interview study (n = 30). Our study shows that many Bangladeshi women, despite being supportive of the movement, did not participate in this movement because of several social, cultural, and infrastructural reasons. We use transnational feminism as a theoretical framework to explain their non-participation. We further discuss how the lessons learned from this study help us better understand the participation, functioning, and impact of social media movements.

The use of tempting and often misleading headlines (clickbait) to allure readers has become a growing practice nowadays among the media outlets. The widespread use of clickbait risks the reader’s trust in media. In this paper, we present BaitBuster, a browser extension and social bot based framework, that detects clickbaits floating on the web, provides brief explanation behind its decision, and regularly makes users aware of potential clickbaits.

The use of alluring headlines (clickbait) to tempt the readers has become a growing practice nowadays. For the sake of existence in the highly competitive media industry, most of the on-line media including the mainstream ones, have started following this practice. Although the wide-spread practice of clickbait makes the reader's reliability on media vulnerable, a large scale analysis to reveal this fact is still absent. In this paper, we analyze 1.67 million Facebook posts created by 153 media organizations to understand the extent of clickbait practice, its impact and user engagement by using our own developed clickbait detection model. The model uses distributed sub-word embeddings learned from a large corpus. The accuracy of the model is 98.3%. Powered with this model, we further study the distribution of topics in clickbait and non-clickbait contents.

Vector representation of sentences is important for many text processing tasks that involve classifying, clustering, or ranking sentences. For solving these tasks, bag-of-word based representation has been used for a long time. In recent years, distributed representation of sentences learned by neural models from unlabeled data has been shown to outperform traditional bag-of-words representations. However, most existing methods belonging to the neural models consider only the content of a sentence, and disregard its relations with other sentences in the context. In this paper, we first characterize two types of contexts depending on their scope and utility. We then propose two approaches to incorporate contextual information into content-based models. We evaluate our sentence representation models in a setup, where context is available to infer sentence vectors. Experimental results demonstrate that our proposed models outshine existing models on three fundamental tasks, such as, classifying, clustering, and ranking sentences.

This paper introduces how ClaimBuster, a fact-checking platform, uses natural language processing and supervised learning to detect important factual claims in political discourses. The claim spotting model is built using a human-labeled dataset of check-worthy factual claims from the U.S. general election debate transcripts. The paper explains the architecture and the components of the system and the evaluation of the model. It presents a case study of how ClaimBuster live covers the 2016 U.S. presidential election debates and monitors social media and Australian Hansard for factual claims. It also describes the current status and the long-term goals of ClaimBuster as we keep developing and expanding it.

Our society is struggling with an unprecedented amount of falsehoods, hyperboles, and half-truths. Politicians and organizations repeatedly make the same false claims. Fake news floods the cyberspace and even allegedly influenced the 2016 election. In fighting false information, the number of active fact-checking organizations has grown from 44 in 2014 to 114 in early 2017. 1 Fact-checkers vet claims by investigating relevant data and documents and publish their verdicts. For instance, PolitiFact.com, one of the earliest and most popular fact-checking projects, gives factual claims truthfulness ratings such as True, Mostly True, Half true, Mostly False, False, and even "Pants on Fire". In the U.S., the election year made fact-checking a part of household terminology. For example, during the first presidential debate on September 26, 2016, NPR.org's live fact-checking website drew 7.4 million page views and delivered its biggest traffic day ever.

This is the first study of crowdsourcing Pareto-optimal object finding over partial orders and by pairwise comparisons, which has applications in public opinion collection, group decision making, and information exploration. Departing from prior studies on crowdsourcing skyline and ranking queries, it considers the case where objects do not have explicit attributes and preference relations on objects are strict partial orders. The partial orders are derived by aggregating crowdsourcers' responses to pairwise comparison questions. The goal is to find all Pareto-optimal objects by the fewest possible questions. It employs an iterative question-selection framework. Guided by the principle of eagerly identifying non-Pareto optimal objects, the framework only chooses candidate questions which must satisfy three conditions. This design is both sufficient and efficient, as it is proven to find a short terminal question sequence. The framework is further steered by two ideas---macro-ordering and micro-ordering. By different micro-ordering heuristics, the framework is instantiated into several algorithms with varying power in pruning questions. Experiment results using both real crowdsourcing marketplace and simulations exhibited not only orders of magnitude reductions in questions when compared with a brute-force approach, but also close-to-optimal performance from the most efficient instantiation.

Public figures such as politicians make claims about "facts" all the time. Journalists and citizens spend a good amount of time checking the veracity of such claims. Toward automatic fact checking, we developed tools to find check-worthy factual claims from natural language sentences. Specifically, we prepared a U.S. presidential debate dataset and built classification models to distinguish check-worthy factual claims from non-factual claims and unimportant factual claims. We also identified the most-effective features based on their impact on the classification models' accuracy.

CrewScout is an expert-team finding system based on the concept of skyline teams and efficient algorithms for finding such teams. Given a set of experts, CrewScout finds all k-expert skyline teams, which are not dominated by any other k-expert teams. The dominance between teams is governed by comparing their aggregated expertise vectors. The need for finding expert teams prevails in applications such as question answering, crowdsourcing, panel selection, and project team formation. The new contributions of this paper include an end-to-end system with an interactive user interface that assists users in choosing teams and an demonstration of its application domains.

We study the novel problem of finding new, prominent situational facts, which are emerging statements about objects that stand out within certain contexts. Many such facts are newsworthy-e.g., an athlete's outstanding performance in a game, or a viral video's impressive popularity. Effective and efficient identification of these facts assists journalists in reporting, one of the main goals of computational journalism. Technically, we consider an ever-growing table of objects with dimension and measure attributes. A situational fact is a “contextual” skyline tuple that stands out against historical tuples in a context, specified by a conjunctive constraint involving dimension attributes, when a set of measure attributes are compared. New tuples are constantly added to the table, reflecting events happening in the real world. Our goal is to discover constraint-measure pairs that qualify a new tuple as a contextual skyline tuple, and discover them quickly before the event becomes yesterday's news. A brute-force approach requires exhaustive comparison with every tuple, under every constraint, and in every measure subspace. We design algorithms in response to these challenges using three corresponding ideas-tuple reduction, constraint pruning, and sharing computation across measure subspaces. We also adopt a simple prominence measure to rank the discovered facts when they are numerous. Experiments over two real datasets validate the effectiveness and efficiency of our techniques.

Towards computational journalism, we present FactWatcher, a system that helps journalists identify data-backed, attention-seizing facts which serve as leads to news stories. FactWatcher discovers three types of facts, including situational facts, one-of-the-few facts, and prominent streaks, through a unified suite of data model, algorithm framework, and fact ranking measure. Given an append-only database, upon the arrival of a new tuple, FactWatcher monitors if the tuple triggers any new facts. Its algorithms efficiently search for facts without exhaustively testing all possible ones. Furthermore, FactWatcher provides multiple features in striving for an end-to-end system, including fact ranking, fact-to-statement translation and keyword-based fact search.

We formulate and investigate the novel problem of finding the skyline k-tuple groups from an n-tuple data set-i.e., groups of k tuples which are not dominated by any other group of equal size, based on aggregate-based group dominance relationship. The major technical challenge is to identify effective anti-monotonic properties for pruning the search space of skyline groups. To this end, we first show that the anti-monotonic property in the well-known Apriori algorithm does not hold for skyline group pruning. Then, we identify two anti-monotonic properties with varying degrees of applicability: order-specific property which applies to SUM, MIN, and MAX as well as weak candidate-generation property which applies to MIN and MAX only. Experimental results on both real and synthetic data sets verify that the proposed algorithms achieve orders of magnitude performance gain over the baseline method.

We formulate and investigate the novel problem of finding the skyline k-tuple groups from an n-tuple dataset - i.e., groups of k tuples which are not dominated by any other group of equal size, based on aggregate-based group dominance relationship. The major technical challenge is to identify effective anti-monotonic properties for pruning the search space of skyline groups. To this end, we show that the anti-monotonic property in the well-known Apriori algorithm does not hold for skyline group pruning. We then identify order-specific property which applies to SUM, MIN, and MAX and weak candidate-generation property which applies to MIN and MAX only. Experimental results on both real and synthetic datasets verify that the proposed algorithms achieve orders of magnitude performance gain over a baseline method.

IEEE 802.11 Medium Access Control (MAC) protocol employs two techniques for packet transmission; the basic access scheme and the RTS/CTS-based reservation scheme. A parameter called RTS Threshold determines which scheme to use. If packet size is smaller than the RTS Threshold then the basic scheme is used otherwise the reservation scheme is used. With current standard, the RTS Threshold is fixed. In this paper, we, first point out the advantages and disadvantages of RTS/CTS based scheme. Then we state the problems of having a fixed RTS Threshold. Next, we present a numerical way to fix the RTS Threshold adaptively based on network traffic. The proposed adaptive scheme creates a balance between the basic scheme and the RTS/CTS based scheme and optimizes the network throughput. Considering multi-hop networks with hidden node problems we have validated our proposal through simulation.