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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.

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.

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.

An increasing number of American adults are consuming news on social media platforms. However, these digital platforms employ personalized recommendation algorithms to increase user engagement. This selective exposure to information leads to the formation of filter bubbles where the users are constantly fed information in line with their ideological views. In this paper, we aim at bursting these filter bubbles through diverse exposure. We discuss the design of a Google Chrome extension, Pop, which augments user's Twitter feeds with news tweets from agencies of different ideological standings.

There is evidence that Russia's Internet Research Agency attempted to interfere with the 2016 U.S. election by running fake accounts on Twitter - often referred to as "Russian trolls". In this work, we: 1) develop machine learning models that predict whether a Twitter account is a Russian troll within a set of 170K control accounts; and, 2) demonstrate that it is possible to use this model to find active accounts on Twitter still likely acting on behalf of the Russian state. Using both behavioral and linguistic features, we show that it is possible to distinguish between a troll and a non-troll with a precision of 78.5% and an AUC of 98.9%, under cross-validation. Applying the model to out-of-sample accounts still active today, we find that up to 2.6% of top journalists' mentions are occupied by Russian trolls. These findings imply that the Russian trolls are very likely still active today. Additional analysis shows that they are not merely software-controlled bots, and manage their online identities in various complex ways. Finally, we argue that if it is possible to discover these accounts using externally - accessible data, then the platforms - with access to a variety of private internal signals - should succeed at similar or better rates.

Online harassment on social media platforms is a pressing matter for which users are not well equipped to handle or avoid. Twitter users can take advantage of third-party websites to get more detailed metrics about the health of another users account, or they can make reports to Twitter after experiencing abuse, but neither option easily allows them to avoid abusive users. We present Tweety Holmes, which analyzes the word usage of Twitter profiles, detects potentially abusive behavior and then warns users visually. It follows principles of algorithmic transparency by visually indicating which words or tweets flagged the profile as abusive so users can better understand the context and also alerts users when they are mentioned or messages by a potentially abusive user with the hopes that this early warning can prevent unhealthy interactions from taking place in the future.

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.

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.

Many social media platforms provide features for commenting, which often provide affordances for people to give feedback to others. Around the theme of advancing research about online comments, this workshop aims to bring together a diverse range of researchers for cross-discipline community building and especially dedicated time for productive, early mixed-methods research. The workshop is structured around a research "hackathon," where participants actively work on producing initial stages of research, theory, and design related to a central dataset (in the case of this workshop, public comments from a historical reddit.com corpus). Participants will form collaborative teams to tackle questions of interest that move forward current thinking around online comment behaviors, participants, quality, and design. The one-day workshop is led by 5 researchers, and it will accommodate up to 32 participants.

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.

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.

Many lab studies have shown that colors can evoke powerful emotions and impact human behavior. Might these phenomena drive how we act online? A key research challenge for image-sharing communities is uncovering the mechanisms by which content spreads through the community. In this paper, we investigate whether there is link between color and diffusion. Drawing on a corpus of one million images crawled from Pinterest, we find that color significantly impacts the diffusion of images and adoption of content on image sharing communities such as Pinterest, even after partially controlling for network structure and activity. Specifically, Red, Purple and pink seem to promote diffusion, while Green, Blue, Black and Yellow suppress it. To our knowledge, our study is the first to investigate how colors relate to online user behavior. In addition to contributing to the research conversation surrounding diffusion, these findings suggest future work using sophisticated computer vision techniques. We conclude with a discussion on the theoretical, practical and design implications suggested by this work-e.g. design of engaging image filters.

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.

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.

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.

With the widespread proliferation of social media tools such as Facebook and Twitter, the CSCW community has seen a growing interest among researchers to turn to records of social behavior from blogs, social media, and social networking sites, to study human social behavior. This nascent area, that has begun to be referred to in various research circles as "computational social science", provides an exciting and promising development in the CSCW community. This panel brings together a host of researchers with varied disciplinary perspectives to investigate the potential and current state-of-the-art, as well as forthcoming challenges, practical and methodological issues that engender this emerging topic.

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.

How do journalists mark quoted content as certain or uncertain, and how do readers interpret these signals? Predicates such as thinks, claims, and admits offer a range of options for framing quoted content according to the author’s own perceptions of its credibility. We gather a new dataset of direct and indirect quotes from Twitter, and obtain annotations of the perceived certainty of the quoted statements. We then compare the ability of linguistic and extra-linguistic features to predict readers’ assessment of the certainty of quoted content. We see that readers are indeed influenced by such framing devices — and we find no evidence that they consider other factors, such as the source, journalist, or the content itself. In addition, we examine the impact of specific framing devices on perceptions of credibility.

In the last decade, we've seen social media explode on the internet. Of course, some of the most popular sites hinge on multimedia. Think Flickr, Instagram, r/pics, Soundcloud. While researchers have shed considerable light on how text and networks work in these spaces, by comparison we know very little about how people use multimedia socially. In this talk, I will sketch some of the opportunities for research in this rich space (potentially by applying findings from computer vision and machine listening), and include some very preliminary findings from my own lab's work.

Modern social media have increasingly helped people separate themselves by worldview. We watch television shows and follow blogs that agree with our views, and read Twitter streams of people we like. The result is often called the echo chamber. Scholars cite political echo chambers as partly to blame for the divisive and destructive U.S. political climate. In this paper, we introduce a mobile application called Political Blend designed to combat echo chambers: it brings people with different political beliefs together for a cup of coffee. Based on interviews, we discovered that people are open to this kind of application and feel it may help the broader political environment. The primary contribution of this work is evidence that people are open to meeting those different from them, even those who ideologically oppose them. In an environment dominated by applications matching based on similarities, we see that this is an important finding.

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.

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.

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.

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.