How Much Hate with #china? A Preliminary Analysis on China-related Hateful Tweets Two Years After the Covid Pandemic Began

• URL: http://arxiv.org/abs/2211.06116v1
• Date: Fri, 11 Nov 2022 10:48:00 GMT
• Title: How Much Hate with #china? A Preliminary Analysis on China-related Hateful Tweets Two Years After the Covid Pandemic Began
• Authors: Jinghua Xu, Zarah Weiss
• Abstract summary: Donald Trump's ''Chinese Virus'' tweet shifted the blame for the spread of the Covid-19 virus to China and the Chinese people. This research intends to examine China-related hate speech on Twitter during the two years following the burst of the pandemic. We identify a hateful rate in #china tweets of 2.5% in 2020 and 1.9% in 2021.
• Score: 1.713291434132985
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Following the outbreak of a global pandemic, online content is filled with hate speech. Donald Trump's ''Chinese Virus'' tweet shifted the blame for the spread of the Covid-19 virus to China and the Chinese people, which triggered a new round of anti-China hate both online and offline. This research intends to examine China-related hate speech on Twitter during the two years following the burst of the pandemic (2020 and 2021). Through Twitter's API, in total 2,172,333 tweets hashtagged #china posted during the time were collected. By employing multiple state-of-the-art pretrained language models for hate speech detection, we identify a wide range of hate of various types, resulting in an automatically labeled anti-China hate speech dataset. We identify a hateful rate in #china tweets of 2.5% in 2020 and 1.9% in 2021. This is well above the average rate of online hate speech on Twitter at 0.6% identified in Gao et al., 2017. We further analyzed the longitudinal development of #china tweets and those identified as hateful in 2020 and 2021 through visualizing the daily number and hate rate over the two years. Our keyword analysis of hate speech in #china tweets reveals the most frequently mentioned terms in the hateful #china tweets, which can be used for further social science studies.

Related papers

• ProvocationProbe: Instigating Hate Speech Dataset from Twitter [0.39052860539161904]
  textitProvocationProbe is a dataset designed to explore what distinguishes instigating hate speech from general hate speech. For this study, we collected around twenty thousand tweets from Twitter, encompassing a total of nine global controversies.
  arXiv  Detail & Related papers  (2024-10-25T16:57:59Z)
• Twitter Dataset on the Russo-Ukrainian War [68.713984286035]
  We have initiated an ongoing dataset acquisition from Twitter API. The dataset has reached the amount of 57.3 million tweets, originating from 7.7 million users. We apply an initial volume and sentiment analysis, while the dataset can be used to further exploratory investigation towards topic analysis, hate speech, propaganda recognition, or even show potential malicious entities like botnets.
  arXiv  Detail & Related papers  (2022-04-07T12:33:06Z)
• 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)
• Detecting Propaganda on the Sentence Level during the COVID-19 Pandemic [0.0]
  malicious cyber-enabled actions may cause increasing social polarization, health crises, and property loss. Using fine-tuned contextualized embedding trained on Reddit, we tackle the detection of the propaganda of such user accounts. Our result shows that the pro-China group appeared to be tweeting 35 to 115 times more than the neutral group.
  arXiv  Detail & Related papers  (2021-07-31T06:40:17Z)
• 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)
• Hate is the New Infodemic: A Topic-aware Modeling of Hate Speech Diffusion on Twitter [24.94135874070525]
  We focus on exploring user behaviour, which triggers the genesis of hate speech on Twitter. We crawl a large-scale dataset of tweets, retweets, user activity history, and follower networks. We characterize different signals of information that govern these dynamics.
  arXiv  Detail & Related papers  (2020-10-09T05:43:08Z)
• Analyzing COVID-19 on Online Social Media: Trends, Sentiments and Emotions [44.92240076313168]
  We analyze the affective trajectories of the American people and the Chinese people based on Twitter and Weibo posts between January 20th, 2020 and May 11th 2020. By contrasting two very different countries, China and the Unites States, we reveal sharp differences in people's views on COVID-19 in different cultures. Our study provides a computational approach to unveiling public emotions and concerns on the pandemic in real-time, which would potentially help policy-makers better understand people's need and thus make optimal policy.
  arXiv  Detail & Related papers  (2020-05-29T09:24:38Z)
• 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)
• Evaluating Online Public Sentiments towards China: A Case Study of English and Chinese Twitter Discourse during the 2019 Chinese National Day [0.8594140167290097]
  The current study describes an approach to analyze online public sentiments using social media data. Over 300,000 tweets were collected between Sept 30 and Oct 3, and a hybrid method of SVM and dictionary was applied to evaluate the sentiments of the collected tweets. The results indicate alignment between the time of National Day celebration activities and the expressed sentiments revealed in both English and Chinese tweets.
  arXiv  Detail & Related papers  (2020-01-13T02:09:45Z)

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.