An influencer-based approach to understanding radical right viral tweets

• URL: http://arxiv.org/abs/2109.07588v1
• Date: Wed, 15 Sep 2021 21:40:25 GMT
• Title: An influencer-based approach to understanding radical right viral tweets
• Authors: Laila Sprejer, Helen Margetts, Kleber Oliveira, David O'Sullivan, Bertie Vidgen
• Abstract summary: ROT 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. It is crucial to account for the influencer-level structure, and find evidence of the importance of both influencer- and content-level factors.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• ManiTweet: A New Benchmark for Identifying Manipulation of News on Social Media [74.93847489218008]
  We present a novel task, identifying manipulation of news on social media, which aims to detect manipulation in social media posts and identify manipulated or inserted information. To study this task, we have proposed a data collection schema and curated a dataset called ManiTweet, consisting of 3.6K pairs of tweets and corresponding articles. Our analysis demonstrates that this task is highly challenging, with large language models (LLMs) yielding unsatisfactory performance.
  arXiv  Detail & Related papers  (2023-05-23T16:40:07Z)
• ViralBERT: A User Focused BERT-Based Approach to Virality Prediction [11.992815669875924]
  We propose ViralBERT, which can be used to predict the virality of tweets using content- and user-based features. We employ a method of concatenating numerical features such as hashtags and follower numbers to tweet text, and utilise two BERT modules. We collect a dataset of 330k tweets to train ViralBERT and validate the efficacy of our model using baselines from current studies in this field.
  arXiv  Detail & Related papers  (2022-05-17T21:40:24Z)
• Manipulating Twitter Through Deletions [64.33261764633504]
  Research into influence campaigns on Twitter has mostly relied on identifying malicious activities from tweets obtained via public APIs. Here, we provide the first exhaustive, large-scale analysis of anomalous deletion patterns involving more than a billion deletions by over 11 million accounts. We find that a small fraction of accounts delete a large number of tweets daily. First, limits on tweet volume are circumvented, allowing certain accounts to flood the network with over 26 thousand daily tweets. Second, coordinated networks of accounts engage in repetitive likes and unlikes of content that is eventually deleted, which can manipulate ranking algorithms.
  arXiv  Detail & Related papers  (2022-03-25T20:07:08Z)
• Influencing the Influencers: Evaluating Person-to-Person Influence on Social Networks Using Granger Causality [6.458496335718509]
  We introduce a novel method for analyzing person-to-person content influence on Twitter. Using an Ego-Alter framework and Granger Causality, we examine President Donald Trump (the Ego) and the people he retweets (Alters) We find that each Alter has a different scope of influence across multiple topics, different magnitude of influence on a given topic, and the magnitude of a single Alter's influence can vary across topics.
  arXiv  Detail & Related papers  (2021-10-10T20:40:11Z)
• News consumption and social media regulations policy [70.31753171707005]
  We analyze two social media that enforced opposite moderation methods, Twitter and Gab, to assess the interplay between news consumption and content regulation. Our results show that the presence of moderation pursued by Twitter produces a significant reduction of questionable content. The lack of clear regulation on Gab results in the tendency of the user to engage with both types of content, showing a slight preference for the questionable ones which may account for a dissing/endorsement behavior.
  arXiv  Detail & Related papers  (2021-06-07T19:26:32Z)
• Divided We Rule: Influencer Polarization on Twitter During Political Crises in India [5.861653129876749]
  We use Google's Universal Sentence (USE) to encode the tweets of 6k influencers and 26k Indian politicians during political crises in India. We obtain aggregate vector representations of the influencers based on their tweet embeddings. We find that while on COVID-19 there is a confluence of influencers on the side of the government, on three other contentious issues around citizenship, Kashmir's statehood, and farmers' protests, it is mainly government-aligned fan accounts that amplify the incumbent's positions.
  arXiv  Detail & Related papers  (2021-05-18T08:38:16Z)
• Cross-Domain Learning for Classifying Propaganda in Online Contents [67.10699378370752]
  We present an approach to leverage cross-domain learning, based on labeled documents and sentences from news and tweets, as well as political speeches with a clear difference in their degrees of being propagandistic. Our experiments demonstrate the usefulness of this approach, and identify difficulties and limitations in various configurations of sources and targets for the transfer step.
  arXiv  Detail & Related papers  (2020-11-13T10:19:13Z)
• Examining the Ordering of Rhetorical Strategies in Persuasive Requests [58.63432866432461]
  We use a Variational Autoencoder model to disentangle content and rhetorical strategies in textual requests from a large-scale loan request corpus. We find that specific (orderings of) strategies interact uniquely with a request's content to impact success rate, and thus the persuasiveness of a request.
  arXiv  Detail & Related papers  (2020-10-09T15:10:44Z)
• Information Consumption and Social Response in a Segregated Environment: the Case of Gab [74.5095691235917]
  This work provides a characterization of the interaction patterns within Gab around the COVID-19 topic. We find that there are no strong statistical differences in the social response to questionable and reliable content. Our results provide insights toward the understanding of coordinated inauthentic behavior and on the early-warning of information operation.
  arXiv  Detail & Related papers  (2020-06-03T11:34:25Z)
• That Message Went Viral?! Exploratory Analytics and Sentiment Analysis into the Propagation of Tweets [0.0]
  We conducted an exploratory analysis on a dataset of over a million Tweets. We identified the most popular messages, and analyzed the tweets on multiple endogenous dimensions. We found some interesting patterns and uncovered new insights to help researchers and practitioners better understand the behavior of popular viral tweets.
  arXiv  Detail & Related papers  (2020-04-21T02:38:53Z)
• Behavior variations and their implications for popularity promotions: From elites to mass in Weibo [11.827654711875256]
  This study focuses on the role of online user influence in determining content popularity. From a novel view of seven content-domains, a detailed picture of behavior variations among five user groups is drawn in Weibo, one of the most popular Twitter-like services in China. The most surprising finding is that the diversity of contents do not always bring more retweets, and the mass and elites should promote content popularity by increasing their retweeter counts and loyalty, respectively.
  arXiv  Detail & Related papers  (2020-04-12T11:49:03Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.