Retweet communities reveal the main sources of hate speech

• URL: http://arxiv.org/abs/2105.14898v1
• Date: Mon, 31 May 2021 11:43:19 GMT
• Title: Retweet communities reveal the main sources of hate speech
• Authors: Bojan Evkoski, Andraz Pelicon, Igor Mozetic, Nikola Ljubesic, Petra Kralj Novak
• Abstract summary: We deploy advanced deep learning to produce high quality hate speech classification models. We create retweet networks, detect communities and monitor their evolution through time. Hate speech is dominated by offensive tweets, related to political and ideological issues. About 60% of unacceptable tweets are produced by a single right-wing community of only moderate size.
• Score: 0.6999740786886536
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We address a challenging problem of identifying main sources of hate speech on Twitter. On one hand, we carefully annotate a large set of tweets for hate speech, and deploy advanced deep learning to produce high quality hate speech classification models. On the other hand, we create retweet networks, detect communities and monitor their evolution through time. This combined approach is applied to three years of Slovenian Twitter data. We report a number of interesting results. Hate speech is dominated by offensive tweets, related to political and ideological issues. The share of unacceptable tweets is moderately increasing with time, from the initial 20% to 30% by the end of 2020. Unacceptable tweets are retweeted significantly more often than acceptable tweets. About 60% of unacceptable tweets are produced by a single right-wing community of only moderate size. Institutional Twitter accounts and media accounts post significantly less unacceptable tweets than individual accounts. However, the main sources of unacceptable tweets are anonymous accounts, and accounts that were suspended or closed during the last three years.

Related papers

• Russo-Ukrainian War: Prediction and explanation of Twitter suspension [47.61306219245444]
  This study focuses on the Twitter suspension mechanism and the analysis of shared content and features of user accounts that may lead to this. We have obtained a dataset containing 107.7M tweets, originating from 9.8 million users, using Twitter API. Our results reveal scam campaigns taking advantage of trending topics regarding the Russia-Ukrainian conflict for Bitcoin fraud, spam, and advertisement campaigns.
  arXiv  Detail & Related papers  (2023-06-06T08:41:02Z)
• Predicting Hate Intensity of Twitter Conversation Threads [26.190359413890537]
  We propose DRAGNET++, which aims to predict the intensity of hatred that a tweet can bring in through its reply chain in the future. It uses the semantic and propagating structure of the tweet threads to maximize the contextual information leading up to and the fall of hate intensity at each subsequent tweet. We show that DRAGNET++ outperforms all the state-of-the-art baselines significantly.
  arXiv  Detail & Related papers  (2022-06-16T18:51:36Z)
• 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)
• Negativity Spreads Faster: A Large-Scale Multilingual Twitter Analysis on the Role of Sentiment in Political Communication [7.136205674624813]
  This paper attempts to analyse tweets of politicians from three European countries. By utilising state-of-the-art pre-trained language models, we performed sentiment analysis on hundreds of thousands of tweets. Our analysis indicates that politicians' negatively charged tweets spread more widely, especially in more recent times.
  arXiv  Detail & Related papers  (2022-02-01T13:25:19Z)
• Comparing the Language of QAnon-related content on Parler, Gab, and Twitter [68.8204255655161]
  Parler, a "free speech" platform popular with conservatives, was taken offline in January 2021 due to the lack of moderation of hateful and QAnon- and other conspiracy-related content. We compare posts with the hashtag #QAnon on Parler over a month-long period with posts on Twitter and Gab. Gab has the highest proportion of #QAnon posts with hate terms, and Parler and Twitter are similar in this respect. On all three platforms, posts mentioning female political figures, Democrats, or Donald Trump have more anti-social language than posts mentioning male politicians, Republicans, or
  arXiv  Detail & Related papers  (2021-11-22T11:19:15Z)
• A Dataset of State-Censored Tweets [3.0254442724635173]
  We release a dataset of 583,437 tweets by 155,715 users that were censored between 2012-2020 July. We also release 4,301 accounts that were censored in their entirety. Our dataset will not only aid in the study of government censorship but will also aid in studying hate speech detection and the effect of censorship on social media users.
  arXiv  Detail & Related papers  (2021-01-15T00:18:27Z)
• Understanding the Hoarding Behaviors during the COVID-19 Pandemic using Large Scale Social Media Data [77.34726150561087]
  We analyze the hoarding and anti-hoarding patterns of over 42,000 unique Twitter users in the United States from March 1 to April 30, 2020. We find the percentage of females in both hoarding and anti-hoarding groups is higher than that of the general Twitter user population. The LIWC anxiety mean for the hoarding-related tweets is significantly higher than the baseline Twitter anxiety mean.
  arXiv  Detail & Related papers  (2020-10-15T16:02:25Z)
• Adoption of Twitter's New Length Limit: Is 280 the New 140? [20.146636010105926]
  In November 2017, Twitter doubled the maximum allowed tweet length from 140 to 280 characters. We analyze Twitter's publicly available 1% sample over a period of around 3 years. We find that, when the length limit was raised from 140 to 280 characters, the prevalence of tweets around 140 characters dropped immediately. Despite this rise, tweets approaching the length limit have been far less frequent after than before the switch.
  arXiv  Detail & Related papers  (2020-09-16T13:01:05Z)
• Racism is a Virus: Anti-Asian Hate and Counterspeech in Social Media during the COVID-19 Crisis [51.39895377836919]
  COVID-19 has sparked racism and hate on social media targeted towards Asian communities. We study the evolution and spread of anti-Asian hate speech through the lens of Twitter. We create COVID-HATE, the largest dataset of anti-Asian hate and counterspeech spanning 14 months.
  arXiv  Detail & Related papers  (2020-05-25T21:58:09Z)
• Russian trolls speaking Russian: Regional Twitter operations and MH17 [68.8204255655161]
  In 2018, Twitter released data on accounts identified as Russian trolls. We analyze the Russian-language operations of these trolls. We find that trolls' information campaign on the MH17 crash was the largest in terms of tweet count.
  arXiv  Detail & Related papers  (2020-05-13T19:48:12Z)

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.