1,720,970 research outputs found
Content attribution ignoring content
No full textCan we tell the author of a message, without reading the message? This work tackles authorship analysis through features that ignore the explicit content of a contribution - informally, those that can be computed even if every character in the body of a message (but not metadata such as timing or \likes") is replaced by an X. Focusing on forum posts, we distil a case-study set of these content-agnostic features, and prove its viability for authorship verification and attribution, using data from four online forums (of different size, language, and topic). A simple classification testbed, relying exclusively on content-agnostic features, confirms the author of a message with 76% accuracy, and discriminates between two candidate authors with 94% accuracy. Being able to re-identify a user without looking at the content of her contributions poses a serious threat to common data anonymization practices
“The government spies using our webcams:” The language of conspiracy theories in online discussions
No full textConspiracy theories are omnipresent in online discussions—whether to explain a late-breaking event that still lacks official report or to give voice to political dissent. Conspiracy theories evolve, multiply, and interconnect, further complicating efforts to limit their propagation. It is therefore crucial to develop scalable methods to examine the nature of conspiratorial discussions in online communities. What do users talk about when they discuss conspiracy theories online? What are the recurring elements in their discussions? What do these elements tell us about the way users think? This work answers these questions by analyzing over ten years of discussions in r/conspiracy—an online community on Reddit dedicated to conspiratorial discussions. We focus on the key elements of a conspiracy theory: the conspiratorial agents, the actions they perform, and their targets. By computationally detecting agent–action–target triplets in conspiratorial statements, and grouping them into semantically coherent clusters, we develop a notion of narrative-motif to detect recurring patterns of triplets. For example, a narrative-motif such as “governmental agency–controls–communications” appears in diverse conspiratorial statements alleging that governmental agencies control information to nefarious ends. Thus, narrative-motifs expose commonalities between multiple conspiracy theories even when they refer to different events or circumstances. In the process, these representations help us understand how users talk about conspiracy theories and offer us a means to interpret what they talk about. Our approach enables a population-scale study of conspiracy theories in alternative news and social media with implications for understanding their adoption and combating their spread
Conspiracies online: User discussions in a conspiracy community following dramatic events
No full textOnline communities play a crucial role in disseminating conspiracy theories. New theories often emerge in the aftermath of catastrophic events. Despite evidence of their widespread appeal, surprisingly little is known about who participates in these event-specific conspiratorial discussions or how do these discussions evolve over time. We study r/conspiracy, an active Reddit community of more than 200,000 users dedicated to conspiratorial discussions. By focusing on four tragic events and 10 years of discussions, we find three distinct user cohorts: joiners, who never participated in Reddit but joined r/conspiracy only after the event; converts who were active Reddit users but joined r/conspiracy only after the event; and veterans, who are longstanding r/conspiracy members. While joiners and converts have a shorter lifespan in the community in comparison to the veterans, joiners are more active during their shorter tenure, becoming increasingly engaged over time. Finally, to investigate how these events affect users' conspiratorial discussions, we adopted a causal inference approach to analyze user comments around the time of the events. We find that discussions happening after the event exhibit signs of emotional shock, increased language complexity, and simultaneous expressions of certainty and doubtfulness. Our work provides insight on how online communities may detect new conspiracy theories that emerge ensuing dramatic events, and in the process stop them before they spread
SENPAI: Supporting exploratory text analysis through semantic & syntactic pattern inspection
No full textAnalyzing language for social computing tasks requires looking beyond individual words. For example, the word “please” generally signals politeness, but more so together with modal verbs (“could you please...”) than without (“please do this.”). Combining semantics and syntax into rich textual patterns is essential to capturing these nuances. What are the relevant patterns for a task, and how to find them? NLP practitioners choose patterns informed by theory, and find them through computational models. However, few tools allow identifying rich patterns without NLP expertise. We introduce SENPAI, a novel tool that discovers combined semantic and syntactic patterns. SENPAI fuses neural embeddings, dependency parsing, and graph mining to surface patterns directly from data. We apply SENPAI to measure credibility, politeness, and sentiment in text. Quantitatively, models powered by SENPAI perform similarly to theoretically-motivated ones. Qualitatively, SENPAI discovers patterns that are interpretable and meaningful. SENPAI enables building computational models without NLP expertise and discovering new linguistic constructs
Sizing Up the Troll
No full textA few troublemakers often spoil online environments for everyone else. An extremely disruptive type of abuser is the troll, whose malicious activities are relatively non-obvious, and thus difficult to detect and contain - particularly by automated systems. A growing corpus of qualitative research focuses on trolling, and differentiates it from other forms of abuse; however, its findings are not directly actionable into automated systems. On the other hand, quantitative research uses definitions of "troll" that mostly fail to capture what moderators and users consider trolling. We address this gap by giving a quantitative analysis of posts, conversations, and users, specifically sanctioned for trolling in an online forum. Although trolls (unlike most other abusers) hardly stand out in a conversation e.g. in terms of vocabulary, how they interact, rather than what they contribute, provides cues of their malicious intent. Copyright is held by the owner/author(s). Publication rights licensed to ACM
Characterizing the social media news sphere through user co-sharing practices
No full textWe describe the landscape of news sources which share social media audience. We focus on 639 news sources, both credible and questionable, and characterize them according to the audience that shares their articles on Twitter. Based on user co-sharing practices, what communities of news sources emerge? We find four groups: one is home to mainstream, high-circulation sources from all sides of the political spectrum; one to satirical, left-leaning sources; one to bipartisan conspiratorial, pseudo-scientific sources; and one to rightleaning, deliberate misinformation sources. Next, we measure which assessments of credibility, impartiality, and journalistic integrity correspond to social media readers' choices of news sources, and uncover the multifaceted structure of the social news sphere. We show how news articles shared on Twitter differ across the four groups along linguistic and psycholinguistics measures. Further, we find that with a high degree of accuracy (~80%), we can classify in what news community an article belongs to. Our data-driven categorization of news sources will help to navigate the complex landscape of online news and has implications for social media platform maintainers to reliably triage questionable outlets
Quotes reveal community structure and interaction dynamics
No full textWe investigate community structure and interaction dynamics in online discussion forums through the lens of quotes, examining four forums of different size, language, and topic. Quote usage appears to have an important role in aiding intra-thread navigation, uncovers a hidden social structure in communities otherwise lacking all explicit signals (from friends and followers to reputations) of today's online social networks, and can be used to fingerprint and characterize both individual users and entire communities
Community structure and interaction dynamics through the lens of quotes
No full textThis is the first work investigating community structure and interaction dynamics through the lens of quotes in online discussion forums. We examine four forums of different size, language, and topic. Quote usage, which is surprisingly consistent over time and users, appears to have an important role in aiding intra-thread navigation, and uncovers a hidden social" structure in communities otherwise lacking all trappings (from friends and followers to reputations) of today's social networks
Quotes in forum.rpg.net
No full textWe analyse the usage of quotes in forum.rpg.net, the largest online forum on tabletop roleplaying games. Quote usage appears pervasive and surprisingly consistent over time and users; it seems to have a role in aiding intra-thread navigation; and it reveals an underlying "social" structure in a community that otherwise lacks all trappings (from friends and followers to reputations) of today's social networks. This is the first work to investigate community structure and interaction through the lens of quotes in an online forum
What Makes People Join Conspiracy Communities?
No full textWidespread conspiracy theories, like those motivating anti-vaccination attitudes or climate change denial, propel collective action, and bear society-wide consequences. Yet, empirical research has largely studied conspiracy theory adoption as an individual pursuit, rather than as a socially mediated process. What makes users join communities endorsing and spreading conspiracy theories? We leverage longitudinal data from 56 conspiracy communities on Reddit to compare individual and social factors determining which users join the communities. Using a quasi-experimental approach, we first identify 30K future conspiracists?(FC) and30K matched non-conspiracists?(NC). We then provide empirical evidence of the importance of social factors across six dimensions relative to the individual factors by analyzing 6 million Reddit comments and posts. Specifically, in social factors, we find that dyadic interactions with members of the conspiracy communities and marginalization outside of the conspiracy communities are the most important social precursors to conspiracy joining-even outperforming individual factor baselines. Our results offer quantitative backing to understand social processes and echo chamber effects in conspiratorial engagement, with important implications for democratic institutions and online communities
- …
