No Arabic abstract
Platforms like Reddit and Twitter offer internet users an opportunity to talk about diverse issues, including those pertaining to physical and mental health. Some of these forums also function as a safe space for severely distressed mental health patients to get social support from peers. The online community platform Reddits SuicideWatch is one example of an online forum dedicated specifically to people who suffer from suicidal thoughts, or who are concerned about people who might be at risk. It remains to be seen if these forums can be used to understand and model the nature of online social support, not least because of the noisy and informal nature of conversations. Moreover, understanding how a community of volunteering peers react to calls for help in cases of suicidal posts, would help to devise better tools for online mitigation of such episodes. In this paper, we propose an approach to characterise conversations in online forums. Using data from the SuicideWatch subreddit as a case study, we propose metrics at a macroscopic level -- measuring the structure of the entire conversation as a whole. We also develop a framework to measure structures in supportive conversations at a mesoscopic level -- measuring interactions with the immediate neighbours of the person in distress. We statistically show through comparison with baseline conversations from random Reddit threads that certain macro and meso-scale structures in an online conversation exhibit signatures of social support, and are particularly over-expressed in SuicideWatch conversations.
Does engagement with opposing views help break down ideological `echo chambers; or does it backfire and reinforce them? This question remains critical as academics, policymakers and activists grapple with the question of how to regulate political discussion on social media. In this study, we contribute to the debate by examining the impact of opposing views within a major climate change skeptic online community on Reddit. A large sample of posts (N = 3000) was manually coded as either dissonant or consonant which allowed the automated classification of the full dataset of more than 50,000 posts, with codes inferred from linked websites. We find that ideologically dissonant submissions act as a stimulant to activity in the community: they received more attention (comments) than consonant submissions, even though they received lower scores through up-voting and down-voting. Users who engaged with dissonant submissions were also more likely to return to the forum. Consistent with identity theory, confrontation with opposing views triggered activity in the forum, particularly among users that are highly engaged with the community. In light of the findings, theory of social identity and echo chambers is discussed and enhanced.
Online health communities offer the promise of support benefits to users, in particular because these communities enable users to find peers with similar experiences. Building mutually supportive connections between peers is a key motivation for using online health communities. However, a users role in a community may influence the formation of peer connections. In this work, we study patterns of peer connections between two structural health roles: patient and non-professional caregiver. We examine user behavior in an online health community where finding peers is not explicitly supported. This context lets us use social network analysis methods to explore the growth of such connections in the wild and identify users peer communication preferences. We investigated how connections between peers were initiated, finding that initiations are more likely between two authors who have the same role and who are close within the broader communication network. Relationships are also more likely to form and be more interactive when authors have the same role. Our results have implications for the design of systems supporting peer communication, e.g. peer-to-peer recommendation systems.
Parler is as an alternative social network promoting itself as a service that allows to speak freely and express yourself openly, without fear of being deplatformed for your views. Because of this promise, the platform become popular among users who were suspended on mainstream social networks for violating their terms of service, as well as those fearing censorship. In particular, the service was endorsed by several conservative public figures, encouraging people to migrate from traditional social networks. After the storming of the US Capitol on January 6, 2021, Parler has been progressively deplatformed, as its app was removed from Apple/Google Play stores and the website taken down by the hosting provider. This paper presents a dataset of 183M Parler posts made by 4M users between August 2018 and January 2021, as well as metadata from 13.25M user profiles. We also present a basic characterization of the dataset, which shows that the platform has witnessed large influxes of new users after being endorsed by popular figures, as well as a reaction to the 2020 US Presidential Election. We also show that discussion on the platform is dominated by conservative topics, President Trump, as well as conspiracy theories like QAnon.
Online platforms are an increasingly popular tool for people to produce, promote or sell their work. However recent studies indicate that social disparities and biases present in the real world might transfer to online platforms and could be exacerbated by seemingly harmless design choices on the site (e.g., recommendation systems or publicly visible success measures). In this paper we analyze an exclusive online community of teams of design professionals called Dribbble and investigate apparent differences in outcomes by gender. Overall, we find that men produce more work, and are able to show it to a larger audience thus receiving more likes. Some of this effect can be explained by the fact that women have different skills and design different images. Most importantly however, women and men position themselves differently in the Dribbble community. Our investigation of users position in the social network shows that women have more clustered and gender homophilous following relations, which leads them to have smaller and more closely knit social networks. Overall, our study demonstrates that looking behind the apparent patterns of gender inequalities in online markets with the help of social networks and product differentiation helps us to better understand gender differences in success and failure.
Political polarization appears to be on the rise, as measured by voting behavior, general affect towards opposing partisans and their parties, and contents posted and consumed online. Research over the years has focused on the role of the Web as a driver of polarization. In order to further our understanding of the factors behind online polarization, in the present work we collect and analyze Web browsing histories of tens of thousands of users alongside careful measurements of the time spent browsing various news sources. We show that online news consumption follows a polarized pattern, where users visits to news sources aligned with their own political leaning are substantially longer than their visits to other news sources. Next, we show that such preferences hold at the individual as well as the population level, as evidenced by the emergence of clear partisan communities of news domains from aggregated browsing patterns. Finally, we tackle the important question of the role of user choices in polarization. Are users simply following the links proffered by their Web environment, or do they exacerbate partisan polarization by intentionally pursuing like-minded news sources? To answer this question, we compare browsing patterns with the underlying hyperlink structure spanned by the considered news domains, finding strong evidence of polarization in partisan browsing habits beyond that which can be explained by the hyperlink structure of the Web.