Extracting Inter-community Conflicts in Reddit

Anti-social behaviors in social media can happen both at user and community levels. While a great deal of attention is on the individual as an 'aggressor,' the banning of entire Reddit subcommunities (i.e., subreddits) demonstrates that this is a multi-layer concern. Existing research on inter-community conflict has largely focused on specific subcommunities or ideological opponents. However, antagonistic behaviors may be more pervasive and integrate into the broader network. In this work, we study the landscape of conflicts among subreddits by deriving higher-level (community) behaviors from the way individuals are sanctioned and rewarded. By constructing a conflict network, we characterize different patterns in subreddit-to-subreddit conflicts as well as communities of 'co-targeted' subreddits. By analyzing the dynamics of these interactions, we also observe that the conflict focus shifts over time.Comment: 21 pages, 7 figure

#ArsonEmergency and Australia's "Black Summer": Polarisation and misinformation on social media

During the summer of 2019-20, while Australia suffered unprecedented bushfires across the country, false narratives regarding arson and limited backburning spread quickly on Twitter, particularly using the hashtag #ArsonEmergency. Misinformation and bot- and troll-like behaviour were detected and reported by social media researchers and the news soon reached mainstream media. This paper examines the communication and behaviour of two polarised online communities before and after news of the misinformation became public knowledge. Specifically, the Supporter community actively engaged with others to spread the hashtag, using a variety of news sources pushing the arson narrative, while the Opposer community engaged less, retweeted more, and focused its use of URLs to link to mainstream sources, debunking the narratives and exposing the anomalous behaviour. This influenced the content of the broader discussion. Bot analysis revealed the active accounts were predominantly human, but behavioural and content analysis suggests Supporters engaged in trolling, though both communities used aggressive language.Comment: 16 pages, 8 images, presented at the 2nd Multidisciplinary International Symposium on Disinformation in Open Online Media (MISDOOM 2020), Leiden, The Netherlands. Published in: van Duijn M., Preuss M., Spaiser V., Takes F., Verberne S. (eds) Disinformation in Open Online Media. MISDOOM 2020. Lecture Notes in Computer Science, vol 12259. Springer, Cham. https://doi.org/10.1007/978-3-030-61841-4_1

Understanding Potential Cyber-Armies in Elections: A Study of Taiwan

Currently, online social networks are essential platforms for political organizations to monitor public opinion, disseminate information, argue with the opposition, and even achieve spin control. However, once such purposeful/aggressive articles flood social sites, it would be more difficult for users to distinguish which messages to read or to trust. In this paper, we aim to address this issue by identifying potential “cyber-armies/professional users” during election campaigns on social platforms. We focus on human-operated accounts who try to influence public discussions, for instance, by publishing hundreds/thousands of comments to show their support or rejection of particular candidates. To achieve our objectives, we collected activity data over six months from a prominent Taiwan-based social forum before the 2018 national election and applied a series of statistical analyses to screen out potential targets. From the results, we successfully identified several accounts according to distinctive characteristics that corresponded to professional users. According to the findings, users and platforms could realize potential information manipulation and increase the transparency of the online society

Analyzing Norm Violations in Live-Stream Chat

Toxic language, such as hate speech, can deter users from participating in online communities and enjoying popular platforms. Previous approaches to detecting toxic language and norm violations have been primarily concerned with conversations from online forums and social media, such as Reddit and Twitter. These approaches are less effective when applied to conversations on live-streaming platforms, such as Twitch and YouTube Live, as each comment is only visible for a limited time and lacks a thread structure that establishes its relationship with other comments. In this work, we share the first NLP study dedicated to detecting norm violations in conversations on live-streaming platforms. We define norm violation categories in live-stream chats and annotate 4,583 moderated comments from Twitch. We articulate several facets of live-stream data that differ from other forums, and demonstrate that existing models perform poorly in this setting. By conducting a user study, we identify the informational context humans use in live-stream moderation, and train models leveraging context to identify norm violations. Our results show that appropriate contextual information can boost moderation performance by 35\%.Comment: 17 pages, 8 figures, 15 table

#ArsonEmergency and Australia's "Black Summer": Polarisation and misinformation on social media

During the summer of 2019-20, while Australia suffered unprecedented bushfires across the country, false narratives regarding arson and limited backburning spread quickly on Twitter, particularly using the hashtag #ArsonEmergency. Misinformation and bot- and troll-like behaviour were detected and reported by social media researchers and the news soon reached mainstream media. This paper examines the communication and behaviour of two polarised online communities before and after news of the misinformation became public knowledge. Specifically, the Supporter community actively engaged with others to spread the hashtag, using a variety of news sources pushing the arson narrative,while the Opposer community engaged less, retweeted more, and focused its use of URLs to link to mainstream sources, debunking the narratives and exposing the anomalous behaviour. This influenced the content of the broader discussion. Bot analysis revealed the active accounts were predominantly human, but behavioural and content analysis suggests Supporters engaged in trolling, though both communities used aggressive language.Derek Weber, Mehwish Nasim, Lucia Falzon, and Lewis Mitchel

Shaping Online Dialogue: Examining How Community Rules Affect Discussion Structures on Reddit

Community rules play a key part in enabling or constraining the behaviors of members in online communities. However, little is unknown regarding whether and to what degree changing rules actually affects community dynamics. In this paper, we seek to understand how these behavior-governing rules shape the interactions between users, as well as the structure of their discussion. Using the top communities on Reddit (i.e. subreddits), we first contribute a taxonomy of behavior-based rule categories across Reddit. Then, we use a network analysis perspective to discover how changing implementation of different rule categories affects subreddits' user interaction and discussion networks over a 1.5 year period. Our study find several significant effects, including greater clustering among users when subreddits increase rules focused on structural regulation and how restricting allowable content surprisingly leads to more interactions between users. Our findings contribute to research in proactive moderation through rule setting, as well as lend valuable insights for online community designers and moderators to achieve desired community dynamics

Adherence to Misinformation on Social Media Through Socio-Cognitive and Group-Based Processes

Previous work suggests that people's preference for different kinds of information depends on more than just accuracy. This could happen because the messages contained within different pieces of information may either be well-liked or repulsive. Whereas factual information must often convey uncomfortable truths, misinformation can have little regard for veracity and leverage psychological processes which increase its attractiveness and proliferation on social media. In this review, we argue that when misinformation proliferates, this happens because the social media environment enables adherence to misinformation by reducing, rather than increasing, the psychological cost of doing so. We cover how attention may often be shifted away from accuracy and towards other goals, how social and individual cognition is affected by misinformation and the cases under which debunking it is most effective, and how the formation of online groups affects information consumption patterns, often leading to more polarization and radicalization. Throughout, we make the case that polarization and misinformation adherence are closely tied. We identify ways in which the psychological cost of adhering to misinformation can be increased when designing anti-misinformation interventions or resilient affordances, and we outline open research questions that the CSCW community can take up in further understanding this cost

Stocks, memes, and desperation capitalism: an ethnographic case study of r/WallStreetBets

Increased availability of digital technologies, such as consumer-level investing platforms, have democratised the financial services industry. Similarly, social media has provided a new forum for retail investors, individuals who are investing without institutional support, to find and share financial advice. Against the backdrop of institutional distrust spurred by the 2008 economic crisis and the COVID-19 cost-of-living crisis, retail investors increasingly turn to investment communities on social media, viewing the stock market as an escape from financial uncertainty. Though prior research has demonstrated that investments made based on information provided from social media largely results in losses, online investing communities, most notably r/wallstreetbets, have continued to attract new users. In this thesis, I demonstrate how social media influences the behavior of retail investors and explain why they are drawn to social media investing communities despite the high likelihood of incurring losses. To conduct this research, I undergo an ethnographic case study of r/wallstreetbets, an online investing community hosted on Reddit known for anti-institutional meme culture and high-risk investment advice. The results demonstrate that users prone to economic biases have their vulnerabilities amplified by social media. Anti-institutional sentiment spurred by lingering resentment for institutions and their role in creating economic crises causes users to trust unregulated information on social media over the regulated advice provided by banks. The investment advice on social media incentivizes users to pursue unnecessarily high-risk trades, exemplified by ‘YOLO trading’ commonly seen on r/wallstreetbets. This culminates in a theory of desperation capitalism, describing why young people feel forced to adopt high-risk money-making strategies to escape financial precarity. The findings of this thesis can help policy-makers, institutions, and academics understand what draws retail investors to social media for advice, resulting in better strategies to mitigate the impacts of dubious online investment advice