ترغب بنشر مسار تعليمي؟ اضغط هنا

Mapping Moral Valence of Tweets Following the Killing of George Floyd

112   0   0.0 ( 0 )
 نشر من قبل Hunter Priniski
 تاريخ النشر 2021
  مجال البحث الهندسة المعلوماتية
والبحث باللغة English




اسأل ChatGPT حول البحث

The viral video documenting the killing of George Floyd by Minneapolis police officer Derek Chauvin inspired nation-wide protests that brought national attention to widespread racial injustice and biased policing practices towards black communities in the United States. The use of social media by the Black Lives Matter movement was a primary route for activists to promote the cause and organize over 1,400 protests across the country. Recent research argues that moral discussions on social media are a catalyst for social change. This study sought to shed light on the moral dynamics shaping Black Lives Matter Twitter discussions by analyzing over 40,000 Tweets geo-located to Los Angeles. The goal of this study is to (1) develop computational techniques for mapping the structure of moral discourse on Twitter and (2) understand the connections between social media activism and protest.



قيم البحث

اقرأ أيضاً

The murder of George Floyd by police in May 2020 sparked international protests and renewed attention in the Black Lives Matter movement. Here, we characterize ways in which the online activity following George Floyds death was unparalleled in its vo lume and intensity, including setting records for activity on Twitter, prompting the saddest day in the platforms history, and causing George Floyds name to appear among the ten most frequently used phrases in a day, where he is the only individual to have ever received that level of attention who was not known to the public earlier that same week. Further, we find this attention extended beyond George Floyd and that more Black victims of fatal police violence received attention following his death than during other past moments in Black Lives Matters history. We place that attention within the context of prior online racial justice activism by showing how the names of Black victims of police violence have been lifted and memorialized over the last 12 years on Twitter. Our results suggest that the 2020 wave of attention to the Black Lives Matter movement centered past instances of police violence in an unprecedented way, demonstrating the impact of the movements rhetorical strategy to say their names.
197 - Nicholas Botzer , Shawn Gu , 2021
Moral outrage has become synonymous with social media in recent years. However, the preponderance of academic analysis on social media websites has focused on hate speech and misinformation. This paper focuses on analyzing moral judgements rendered o n social media by capturing the moral judgements that are passed in the subreddit /r/AmITheAsshole on Reddit. Using the labels associated with each judgement we train a classifier that can take a comment and determine whether it judges the user who made the original post to have positive or negative moral valence. Then, we use this classifier to investigate an assortment of website traits surrounding moral judgements in ten other subreddits, including where negative moral users like to post and their posting patterns. Our findings also indicate that posts that are judged in a positive manner will score higher.
62 - Dmitry Zinoviev 2020
We applied complex network analysis to ~27,000 tweets posted by the 2016 presidential elections principal participants in the USA. We identified the stages of the election campaigns and the recurring topics addressed by the candidates. Finally, we re vealed the leader-follower relationships between the candidates. We conclude that Secretary Hillary Clintons Twitter performance was subordinate to that of Donald Trump, which may have been one factor that led to her electoral defeat.
Businesses communicate using Twitter for a variety of reasons -- to raise awareness of their brands, to market new products, to respond to community comments, and to connect with their customers and potential customers in a targeted manner. For busin esses to do this effectively, they need to understand which content and structural elements about a tweet make it influential, that is, widely liked, followed, and retweeted. This paper presents a systematic methodology for analyzing commercial tweets, and predicting the influence on their readers. Our model, which use a combination of decoration and meta features, outperforms the prediction ability of the baseline model as well as the tweet embedding model. Further, in order to demonstrate a practical use of this work, we show how an unsuccessful tweet may be engineered (for example, reworded) to increase its potential for success.
Real-time tweets can provide useful information on evolving events and situations. Geotagged tweets are especially useful, as they indicate the location of origin and provide geographic context. However, only a small portion of tweets are geotagged, limiting their use for situational awareness. In this paper, we adapt, improve, and evaluate a state-of-the-art deep learning model for city-level geolocation prediction, and integrate it with a visual analytics system tailored for real-time situational awareness. We provide computational evaluations to demonstrate the superiority and utility of our geolocation prediction model within an interactive system.
التعليقات
جاري جلب التعليقات جاري جلب التعليقات
سجل دخول لتتمكن من متابعة معايير البحث التي قمت باختيارها
mircosoft-partner

هل ترغب بارسال اشعارات عن اخر التحديثات في شمرا-اكاديميا