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Added by
Nga Than
Publication date
2020
fields
Informatics Engineering
and research's language is
English
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Social media has become an important venue for diverse groups to share information, discuss political issues, and organize social movements. Recent scholarship has shown that the social media ecosystem can affect political thinking and expression. Individuals and groups across the political spectrum have engaged in the use of these platforms extensively, even creating their own forums with varying approaches to content moderation in pursuit of freer standards of speech. The Gab social media platform arose in this context. Gab is a social media platform for the so-called alt right, and much of the popular press has opined about the thematic content of discourses on Gab and platforms like it, but little research has examined the content itself. Using a publicly available dataset of all Gab posts from August 2016 until July 2019, the current paper explores a five percent random sample of this dataset to explore thematic content on the platform. We run multiple structural topic models, using standard procedures to arrive at an optimal k number of topics. The final model specifies 85 topics for 403,469 documents. We include as prevalence variables whether the source account has been flagged as a bot and the number of followers for the source account. Results suggest the most nodal topics in the dataset pertain to the authenticity of the Holocaust, the meaning of red pill, and the journalistic merit of mainstream media. We conclude by discussing the implications of our findings for work in ethical content moderation, online community development, political polarization, and avenues for future research.
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Gab is an online social network often associated with the alt-right political movement and users barred from other networks. It presents an interesting opportunity for research because near-complete data is available from day one of the networks creation. In this paper, we investigate the evolution of the user interaction graph, that is the graph where a link represents a user interacting with another user at a given time. We view this graph both at different times and at different timescales. The latter is achieved by using sliding windows on the graph which gives a novel perspective on social network data. The Gab network is relatively slowly growing over the period of months but subject to large bursts of arrivals over hours and days. We identify plausible events that are of interest to the Gab community associated with the most obvious such bursts. The network is characterised by interactions between `strangers rather than by reinforcing links between `friends. Gab usage follows the diurnal cycle of the predominantly US and Europe based users. At off-peak hours the Gab interaction network fragments into sub-networks with absolutely no interaction between them. A small group of users are highly influential across larger timescales, but a substantial number of users gain influence for short periods of time. Temporal analysis at different timescales gives new insights above and beyond what could be found on static graphs.
The moderation of content in many social media systems, such as Twitter and Facebook, motivated the emergence of a new social network system that promotes free speech, named Gab. Soon after that, Gab has been removed from Google Play Store for violating the companys hate speech policy and it has been rejected by Apple for similar reasons. In this paper we characterize Gab, aiming at understanding who are the users who joined it and what kind of content they share in this system. Our findings show that Gab is a very politically oriented system that hosts banned users from other social networks, some of them due to possible cases of hate speech and association with extremism. We provide the first measurement of news dissemination inside a right-leaning echo chamber, investigating a social media where readers are rarely exposed to content that cuts across ideological lines, but rather are fed with content that reinforces their current political or social views.
Most of the information operations involve users who may foster polarization and distrust toward science and mainstream journalism, without these users being conscious of their role. Gab is well known to be an extremist-friendly platform that performs little control on the posted content. Thus it represents an ideal benchmark for studying phenomena potentially related to polarization such as misinformation spreading. The combination of these factors may lead to hate as well as to episodes of harm in the real world. In this work we provide a characterization of the interaction patterns within Gab around the COVID-19 topic. To assess the spreading of different content type, we analyze consumption patterns based on both interaction type and source reliability. Overall we find that there are no strong statistical differences in the social response to questionable and reliable content, both following a power law distribution. However, questionable and reliable sources display structural and topical differences in the use of hashtags. The commenting behaviour of users in terms of both lifetime and sentiment reveals that questionable and reliable posts are perceived in the same manner. We can conclude that despite evident differences between questionable and reliable posts Gab users do not perform such a differentiation thus treating them as a whole. Our results provide insights toward the understanding of coordinated inauthentic behavior and on the early-warning of information operation.
Radical right influencers routinely use social media to spread highly divisive, disruptive and anti-democratic messages. Assessing and countering the challenge that such content poses is crucial for ensuring that online spaces remain open, safe and accessible. Previous work has paid little attention to understanding factors associated with radical right content that goes viral. We investigate this issue with a new dataset ROT which provides insight into the content, engagement and followership of a set of 35 radical right influencers. It includes over 50,000 original entries and over 40 million retweets, quotes, replies and mentions. We use a multilevel model to measure engagement with tweets, which are nested in each influencer. We show that it is crucial to account for the influencer-level structure, and find evidence of the importance of both influencer- and content-level factors, including the number of followers each influencer has, the type of content (original posts, quotes and replies), the length and toxicity of content, and whether influencers request retweets. We make ROT available for other researchers to use.
We investigate the optical and Wide-field Survey Explorer (WISE) colors of E+A identified post-starburst galaxies, including a deep analysis on 190 post-starbursts detected in the 2{mu}m All Sky Survey Extended Source Catalog. The post-starburst galaxies appear in both the optical green valley and the WISE Infrared Transition Zone (IRTZ). Furthermore, we find that post-starbursts occupy a distinct region [3.4]-[4.6] vs. [4.6]-[12] WISE colors, enabling the identification of this class of transitioning galaxies through the use of broad-band photometric criteria alone. We have investigated possible causes for the WISE colors of post-starbursts by constructing a composite spectral energy distribution (SED), finding that mid-infrared (4-12{mu}m) properties of post-starbursts are consistent with either 11.3{mu}m polycyclic aromatic hydrocarbon emission, or Thermally Pulsating Asymptotic Giant Branch (TP-AGB) and post-AGB stars. The composite SED of extended post- starburst galaxies with 22{mu}m emission detected with signal to noise >3 requires a hot dust component to produce their observed rising mid-infrared SED between 12 and 22{mu}m. The composite SED of WISE 22{mu}m non-detections (S/N<3), created by stacking 22{mu}m images, is also flat, requiring a hot dust component. The most likely source of this mid-infrared emission of these E+A galaxies is a buried active galactic nucleus. The inferred upper limit to the Eddington ratios of post-starbursts are 1e-2 to 1e-4, with an average of 1e-3. This suggests that AGNs are not radiatively dominant in these systems. This could mean that including selections able to identify active galactic nuclei as part of a search for transitioning and post-starburst galaxies would create a more complete census of the transition pathways taken as a galaxy quenches its star formation.
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