Wikipedia, the Web's largest encyclopedia, frequently faces content disputes or malicious users seeking to subvert its integrity. Administrators can mitigate such disruptions by enforcing "page protection" that selectively limits contributions to specific articles to help prevent the degradation of content. However, this practice contradicts one of Wikipedia's fundamental principles—that it is open to all contributors—and may hinder further improvement of the encyclopedia. In this paper, we examine the effect of page protection on article quality to better understand whether and when page protections are warranted. Using decade-long data on page protections from the English Wikipedia, we conduct a quasi-experimental study analyzing pages that received "requests for page protection"—written appeals submitted by Wikipedia editors to administrators to impose page protections. We match pages that indeed received page protection with similar pages that did not and quantify the causal effect of the interventions on a well-established measure of article quality. Our findings indicate that the effect of page protection on article quality depends on the characteristics of the page prior to the intervention: high-quality articles are affected positively, as opposed to low-quality articles that are impacted negatively. Subsequent analysis suggests that high-quality articles degrade when left unprotected, whereas low-quality articles improve. Overall, with our study, we outline page protections on Wikipedia and inform best practices on whether and when to protect an article.

Online platforms have sanctioned individuals and communities associated with ‘fringe’ movements linked to hate speech, violence, and terrorism — but can these sanctions contribute to the abandonment of these movements? Here, we investigate this question through the lens of r/exredpill, a recovery community on Reddit meant to help individuals leave movements within the Manosphere, a conglomerate of fringe Web-based movements focused on men’s issues. We conduct an observational study on the impact of sanctioning some of Reddit’s largest Manosphere communities on the activity levels and user influx of r/exredpill, the largest associated recovery subreddit. We find that banning a related radical community positively affects participation in r/exredpill in the period following the ban. Yet, quarantining the community, a softer moderation intervention, yields no such effects. We show that the effect induced by banning a radical community is stronger than for some of the widely discussed real-world events related to the Manosphere and that moderation actions against the Manosphere do not cause a spike in toxicity or malicious activity in r/exredpill. Overall, our findings suggest that content moderation acts as a deradicalization catalyst.

Research using YouTube data often explores social and semantic dimensions of channels and videos. Typically, analyses rely on laborious manual annotation of content and content creators, often found by low-recall methods such as keyword search. Here, we explore an alternative approach, Tube2Vec, using latent representations (embeddings) obtained via machine learning. Using a large dataset of YouTube links shared on Reddit; we create embeddings that capture social sharing behavior, video metadata (title, description, etc.), and YouTube's video recommendations. We evaluate these embeddings using crowdsourcing and existing datasets, finding that recommendation embeddings excel at capturing both social and semantic dimensions, although social-sharing embeddings better correlate with existing partisan scores. We share embeddings capturing the social and semantic dimensions of 44,000 YouTube channels for the benefit of future research on YouTube. https://github.com/epfl-dlab/youtube-embeddings.

Fringe communities promoting conspiracy theories and extremist ideologies have thrived on mainstream platforms, raising questions about the mechanisms driving their growth. Here, we hypothesize and study a possible mechanism: new members may be recruited through fringe-interactions: the exchange of comments between members and non-members of fringe communities. We apply text-based causal inference techniques to study the impact of fringe-interactions on the growth of three prominent fringe communities on Reddit: r/Incel, r/GenderCritical, and r/The Donald. Our results indicate that fringe-interactions attract new members to fringe communities. Users who receive these interactions are up to 4.2 percentage points (pp) more likely to join fringe communities than similar, matched users who do not.This effect is influenced by 1) the characteristics of communities where the interaction happens (e.g., left vs. right-leaning communities) and 2) the language used in the interactions. Interactions using toxic language have a 5pp higher chance of attracting newcomers to fringe communities than non-toxic interactions. We find no effect when repeating this analysis by replacing fringe (r/Incel, r/GenderCritical, and r/The Donald) with non-fringe communities (r/climatechange, r/NBA, r/leagueoflegends), suggesting this growth mechanism is specific to fringe commu- nities. Overall, our findings suggest that curtailing fringe interactions may reduce the growth of fringe communities on mainstream platforms.

According to journalistic standards, direct quotes should be attributed to sources with objective quotatives such as ``said'' and ``told,'' since nonobjective quotatives, e.g., ``argued'' and ``insisted,'' would influence the readers' perception of the quote and the quoted person. In this paper, we analyze the adherence to this journalistic norm to study trends in objectivity in political news across U.S. outlets of different ideological leanings. We ask: 1) How has the usage of nonobjective quotatives evolved? 2) How do news outlets use nonobjective quotatives when covering politicians of different parties? To answer these questions, we developed a dependency-parsing-based method to extract quotatives and applied it to Quotebank, a web-scale corpus of attributed quotes, obtaining nearly 7 million quotes, each enriched with the quoted speaker's political party and the ideological leaning of the outlet that published the quote. We find that, while partisan outlets are the ones that most often use nonobjective quotatives, between 2013 and 2020, the outlets that increased their usage of nonobjective quotatives the most were ``moderate'' centrist news outlets (around 0.6 percentage points, or 20% in relative percentage over seven years). Further, we find that outlets use nonobjective quotatives more often when quoting politicians of the opposing ideology (e.g., left-leaning outlets quoting Republicans) and that this ``quotative bias'' is rising at a swift pace, increasing up to 0.5 percentage points, or 25% in relative percentage, per year. These findings suggest an overall decline in journalistic objectivity in U.S. political news.

Automated audits of recommender systems found that blindly following recommendations leads users to increasingly partisan, conspiratorial, or false content. At the same time, studies using real user traces suggest that recommender systems are not the primary driver of attention toward extreme content; on the contrary, such content is mostly reached through other means, e.g., other websites. In this paper, we explain the following apparent paradox: if the recommendation algorithm favors extreme content, why is it not driving its consumption? With a simple agent-based model where users attribute different utilities to items in the recommender system, we show through simulations that the collaborative-filtering nature of recommender systems and the nicheness of extreme content can resolve the apparent paradox: although blindly following recommendations would indeed lead users to niche content, users rarely consume niche content when given the option because it is of low utility to them, which can lead the recommender system to deamplify such content. Our results call for a nuanced interpretation of "algorithmic amplification" and highlight the importance of modeling the utility of content to users when auditing recommender systems. Code available: https://github.com/epfl-dlab/amplification_paradox.

Online platforms face pressure to keep their communities civil and respectful. Thus, banning problematic online communities from mainstream platforms is often met with enthusiastic public reactions. However, this policy can lead users to migrate to alternative fringe platforms with lower moderation standards and may reinforce antisocial behaviors. As users of these communities often remain co-active across mainstream and fringe platforms, antisocial behaviors may spill over onto the mainstream platform. We study this possible spillover by analyzing 70,000 users from three banned communities that migrated to fringe platforms: r/The_Donald, r/GenderCritical, and r/Incels. Using a difference-in-differences design, we contrast co-active users with matched counterparts to estimate the causal effect of fringe platform participation on users' antisocial behavior on Reddit. Our results show that participating in the fringe communities increases users' toxicity on Reddit (as measured by Perspective API) and involvement with subreddits similar to the banned community---which often also breach platform norms. The effect intensifies with time and exposure to the fringe platform. In short, we find evidence for a spillover of antisocial behavior from fringe platforms onto Reddit via co-participation.

Online social media platforms use automated moderation systems to remove or reduce the visibility of rule-breaking content. While previous work has documented the importance of manual content moderation, the effects of automated content moderation remain largely unknown. Here, in a large study of Facebook comments (n = 412M), we used a fuzzy regression discontinuity design to measure the impact of automated content moderation on subsequent rule-breaking behavior (number of comments hidden/deleted) and engagement (number of additional comments posted). We found that comment deletion decreased subsequent rule-breaking behavior in shorter threads (20 or fewer comments), even among other participants, suggesting that the intervention prevented conversations from derailing. Further, the effect of deletion on the affected user’s subsequent rule-breaking behavior was longer-lived than its effect on reducing commenting in general, suggesting that users were deterred from rule-breaking but not from commenting. In contrast, hiding (rather than deleting) content had small and statistically insignificant effects. Our results suggest that automated content moderation increases adherence to community guidelines.

Information diffusion is usually modeled as a process in which immutable pieces of information propagate over a network. In reality, however, messages are not immutable, but may be morphed with every step, potentially entailing large cumulative distortions. This process may lead to misinformation even in the absence of malevolent actors, and understanding it is crucial for modeling and improving online information systems. Here, we perform a controlled, crowdsourced experiment in which we simulate the propagation of information from medical research papers. Starting from the original abstracts, crowd workers iteratively shorten previously produced summaries to increasingly smaller lengths. We also collect control summaries where the original abstract is compressed directly to the final target length. Comparing cascades to controls allows us to separate the effect of the length constraint from that of accumulated distortion. Via careful manual coding, we annotate lexical and semantic units in the medical abstracts and track them along cascades. We find that iterative summarization has a negative impact due to the accumulation of error, but that high-quality intermediate summaries result in less distorted messages than in the control case. Different types of information behave differently; in particular, the conclusion of a medical abstract (i.e., its key message) is distorted most. Finally, we compare extractive with abstractive summaries, finding that the latter are less prone to semantic distortion. Overall, this work is a first step in studying information cascades without the assumption that disseminated content is immutable, with implications on our understanding of the role of word-of-mouth effects on the misreporting of science.

Professionals outside of the area of Computer Science have an increasing need to analyze large bodies of data. This analysis often demands high level of security and has to be done in the cloud. However, current data analysis tools that demand little proficiency in systems programming struggle to deliver solutions which are scalable and safe. In this context we present Lemonade, a platform which focuses on creating data analysis and mining flows in the cloud, with authentication, authorization and accounting (AAA) guarantees. Lemonade provides an interface for the visual construction of flows, and encapsulates storage and data processing environment details, providing higher-level abstractions for data source access and algorithms. We illustrate its usage through a demo, where a data processing flow builds a classification model for detecting fake-news, also extracting some insights along the way.