A Machine Learning Analysis of Twitter Sentiment to the Sandy Hook Shootings

Gun related violence is a complex issue and accounts for a large proportion of violent incidents. In the research reported in this paper, we set out to investigate the pro-gun and anti-gun sentiments expressed on a social media platform, namely Twitter, in response to the 2012 Sandy Hook Elementary School shooting in Connecticut, USA. Machine learning techniques are applied to classify a data corpus of over 700,000 tweets. The sentiments are captured using a public sentiment score that considers the volume of tweets as well as population. A web-based interactive tool is developed to visualise the sentiments and is available at this http://www.gunsontwitter.com. The key findings from this research are: (i) There are elevated rates of both pro-gun and anti-gun sentiments on the day of the shooting. Surprisingly, the pro-gun sentiment remains high for a number of days following the event but the anti-gun sentiment quickly falls to pre-event levels. (ii) There is a different public response from each state, with the highest pro-gun sentiment not coming from those with highest gun ownership levels but rather from California, Texas and New York

Religious Violence and Twitter: Networks of Knowledge, Empathy and Fascination

Twitter analysis through data mining, text analysis, and visualization, coupled with the application of actor-network-theory, reveals a coalition of heterogenous religious affiliations around grief and fascination. While religious violence has always existed, the prevalence of social media has led to an increase in the magnitude of discussions around the topic. This paper examines the different reactions on Twitter to violence targeting three religious communities: the 2015 Charleston Church shooting, the 2018 Pittsburgh Synagogue shooting, and the 2019 Christchurch Mosque shootings. The attacks were all perpetrated by white nationalists with firearms. By analyzing large Twitter datasets in response to the attacks, we were able to render visible associations among actors across religions communities, national identities, and political persuasions. What this project revealed is that if we apply actor-network-theory and data visualization to look at networks created by human/non-human (text, computer, phone, meme, tweet, retweet, hashtag) actors, we can see that knowledge, empathy, and fascination drive communication around mass violence against religious communities

From Hoax as Crisis to Crisis as Hoax: Fake News and Information Disorder as Disruptions to the Discourse of Renewal

Hoaxes have long been a reputational threat to organizations. For example, false claims that syringes had been found in bottles of Pepsi-Cola products, that a portion of a finger had been found in Wendy’s chili, and that Domino’s employees had intentionally served contaminated food to customers have topped the media’s agenda. More recently, the hoax phenomenon has been tactically reversed. Heavily trafficked Internet sites and controversial television personalities frequently argue that well-documented crises themselves are hoaxes. The potential for claims of crisis as hoax to disrupt the discourse of crisis renewal is examined through an analysis of three cases. We argue that overcoming such disruptions requires corporate social responsibility, a focus on the issues rather than the hoaxers, and continued efforts to improve media literacy for all audiences

Mathematical Modeling of Public Opinion using Traditional and Social Media

With the growth of the internet, data from text sources has become increasingly available to researchers in the form of online newspapers, journals, and blogs. This data presents a unique opportunity to analyze human opinions and behaviors without soliciting the public explicitly. In this research, I utilize newspaper articles and the social media service Twitter to infer self-reported public opinions and awareness of climate change. Climate change is one of the most important and heavily debated issues of our time, and analyzing large-scale text surrounding this issue reveals insights surrounding self-reported public opinion. First, I inquire about public discourse on both climate change and energy system vulnerability following two large hurricanes. I apply topic modeling techniques to a corpus of articles about each hurricane in order to determine how these topics were reported on in the post event news media. Next, I perform sentiment analysis on a large collection of data from Twitter using a previously developed tool called the hedonometer . I use this sentiment scoring technique to investigate how the Twitter community reports feeling about climate change. Finally, I generalize the sentiment analysis technique to many other topics of global importance, and compare to more traditional public opinion polling methods. I determine that since traditional public opinion polls have limited reach and high associated costs, text data from Twitter may be the future of public opinion polling

Examining the Public Response to Vigilantism: A Multi-dimensional Model of Social Media Discourse

This paper examines the social media discourse of two real-world vigilantism incidents that had invited nation-wide debate: Murder of ‘Ahmaud Arbery’ (victim), a racially motivated hate crime and the fatal shooting of two men by ‘Kyle Rittenhouse’ (an aggressor). Both these incidents had invited a lot of debate in social media. However, little is known about the nature of discussions on vigilantism in social media. In this paper, first, through topic modeling, we examine the kind of discussions that were triggered by these incidents. We identify various dimensions of the on-line public conversations. Second, we study if there is polarization in the public discourses. We find that victim-oriented discourse on vigilantism displayed more polarization in a certain dimension and aggressor-oriented discourse on vigilantism displayed more polarization in another dimension. We also found that aggressor-oriented vigilantism discussions had higher negative emotion scores compared to victim-oriented discussion

A Comparative Analysis of Media and Legislative Rhetoric on Gun Control

In this paper, I analyze the use of language in the debates on gun laws in three different countries: the United States, Canada, and the United Kingdom. Through systematic analysis of both legislative debates and media coverage of gun violence and gun control, I identify the major frames utilized by these political elites and news networks when discussing the issue.My findings show that there are indeed measurable differences between the rhetoric used by both legislators and the media. Each country has both shared and unique frames that are used by both types of actors. The rhetoric in the United States is much more complex than in either Canada or the United Kingdom, with frames covering a much larger range of issues than simply gun laws. Rhetoric on gun control and other such legislation was very salient in both the Canadian and British media and legislature after mass shootings, and these changes correlate with legislative changes. The United States has no consistent response in either the media or the legislature after mass shootings, and gun laws are not particularly salient. Interest groups play a large role in the legislative debates in the United States, and are viewed negatively by members of opposing parties, leading to ideological deadlock in terms of gun control