Citizens on Twitter: A Rhetorical Analysis of Emerging Political Satire

This thesis provides an overview of the history of satire, its rhetorical structure, and my interpretation of its historically culminated five fundamental characteristics. I also introduce that the rise in popularity of American political satire through various media has inspired a new wave of American satirists who project their own political satirical messages through social media platforms and how Twitter, in particular, has provided those average individuals with the opportunity to more actively, directly, and satirically take part in political discussions. With a collection of two data sets of tweets—one larger live tweet sweep during the first 2016 presidential debate and a study of five average individual political satirists\u27 tweets throughout a majority of the 2016 presidential campaign and after—I analyze how these tweets command a legitimacy into the established satirical realm because of their adherence to the fundamental characteristics presented. I also analyze how this particular social media platform affects these texts\u27 productions through the challenges presented to satirists and strategies that have emerged to combat those challenges. I then discuss the implications for and opportunities provided to average American citizens as political satirical commentators on Twitter in the changing world of American politics

What factors influence whether politicians’ tweets are retweeted? Using CHAID to build an explanatory model of the retweeting of politicians’ tweets during the 2015 UK General Election campaign

Twitter is ever-present in British political life and many politicians use it as part of their campaign strategies. However, little is known about whether their tweets engage people, for example by being retweeted. This research addresses that gap, examining tweets sent by MPs during the 2015 UK General Election campaign to identify which were retweeted and why. A conceptual model proposes three factors which are most likely to influence retweets: the characteristics of (1) the tweet’s sender, (2) the tweet and (3) its recipients. This research focuses on the first two of these. Content and sentiment analysis are used to develop a typology of the politicians’ tweets, followed by CHAID analysis to identify the factors that best predict which tweets are retweeted. The research shows that the characteristics of tweet and its sender do influence whether the tweet is retweeted. Of the sender’s characteristics, number of followers is the most important – more followers leads to more retweets. Of the tweet characteristics, the tweet’s sentiment is the most influential. Negative tweets are retweeted more than positive or neutral tweets. Tweets attacking opponents or using fear appeals are also highly likely to be retweeted. The research makes a methodological contribution by demonstrating how CHAID models can be used to accurately predict retweets. This method has not been used to predict retweets before and has broad application to other contexts. The research also contributes to our understanding of how politicians and the public interact on Twitter, an area little studied to date, and proposes some practical recommendations regarding how MPs can improve the effectiveness of their Twitter campaigning. The finding that negative tweets are more likely to be retweeted also contributes to the ongoing debate regarding whether people are more likely to pass on positive or negative information online

TWEET AND RETWEET JOURNALISM DURING THE PANDEMIC: dissemination of and engagement with news on Twitter

Starting from a gap identified in the literature regarding the use of social networks by newspapers to disseminate urgent news, this article aims to study strategies of journalistic content in social media, particularly in the context of a public crisis and to compare the effectiveness of different types of news disseminated in this medium, namely in terms of reach and generated interaction. The following research question was defined: how popular was public health news in Brazil during the covid-19 pandemic? Based on contributions in the literature, a quantitative study was carried out, using the content analysis technique. The study enable to better understand the sharing behavior of news in Twitter, the consumption behavior of newspaper readers on social networks and the generation of news during the pandemic

Tweets from the Campaign Trail

Hailed by many as a game-changer in political communication, Twitter has made its way into election campaigns all around the world. The European Parliamentary elections, taking place simultaneously in 28 countries, give us a unique comparative vision of the way the tool is used by candidates in different national contexts. This volume is the fruit of a research project bringing together scholars from 6 countries, specialised in communication science, media studies, linguistics and computer science. It seeks to characterise the way Twitter was used during the 2014 European election campaign, providing insights into communication styles and strategies observed in different languages and outlining methodological solutions for collecting and analysing political tweets in an electoral context

Alternative Realities on Social Media: Twitter and the German Right-Wing

Right-wing radicalism has moved to the periphery of public and academic attention. This is despite the fact that hate crimes are increasing and right-wing populist parties win enough votes to be represented in parliament or even make up governments Europe-wide. In addition, the Internet has made it easy for the right-wing to network and transport their ideology to a large audience. The research, therefore, asks how the German right-wing as a contrast society is portrayed on Twitter networks. Because it is the goal of contrast societies to change the target society in its favor, the Internet offers a space where an ideal society can be created online. Right-wing networks on Twitter will be analyzed with a social media analysis using Twitter statistics, NodeXL Pro and Gephi. The analysis will be conducted by identifying online radicalization themes, like for example hate towards refugees, in the form of hashtags, that constitute the alternative reality created online. The results of the analysis will then be presented in the context of radical contrast society's efforts to change society. This way, it will be possible to come to a meaningful conclusion that includes wider societal implications of the right-wings' networking on Twitter.Katedra bezpečnostních studiíDepartment of Security StudiesFaculty of Social SciencesFakulta sociálních vě

#HashtagSolidarities: Twitter debates and networks in the MENA region

During the course of the so-called Arab Spring, observers were quick to refer to the uprisings as »Facebook revolutions« or »Twitter revolutions«. Although the important role of social media in the 2011 upheavals in the Middle East and North Africa (MENA) is widely acknowledged, its impact on political processes in the region remains contested and contradictory. Rather than looking at social media through a transformation or security lens, the research presented here focused on how debates on three events in the MENA region – the emergence of a video of a rape on Cairo’s Tahrir Square in June 2014, anti-fracking protests in southern Algeria in early 2015, and Saudi Arabia’s military intervention in Yemen in March 2015 – unfolded on Twitter. Closely tracing Twitter debates on these incidents shed light on Twitter’s role in important social and political discussions as well as on the scope and patterns of Twitter networks and digital solidarities. In other words, it highlighted the various ways in which Twitter was used by ordinary people, activists, media outlets, and officials, and in doing so, it provides an idea of the political impact such debates can have via Twitter. The research also revealed that the breadth of opinion on Twitter far exceeds that of traditional media in the MENA region, and the more repressive a context, the more important Twitter becomes. Furthermore, Twitter, in forging digital solidarities, contributes to deepening existing social and political cleavages. That is, the platform is not an autonomous digital space following logics different from those in the physical world. Rather, the dynamics of Twitter are strongly driven by local historical experience, social patterns, and national politics. (Autorenreferat

Using Consumer-Generated Social Media Posts to Improve Forecasts of Television Premiere Viewership: Extending Diffusion of Innovation Theory

Billions of US dollars in transactions occur each year between media companies and advertisers purchasing commercials on television shows to reach target demographics. This study investigates how consumer enthusiasm can be quantified (via social media posts) as an input to improve forecast models of television series premiere viewership beyond inputs that are typically used in the entertainment industry. Results support that Twitter activity (volume of tweets and retweets) is a driver of consumer viewership of unscripted programs (i.e., reality or competition shows). As such, incorporating electronic word of mouth (eWOM) into forecasting models improves accuracy for predictions of unscripted shows. Furthermore, trend analysis suggests it is possible to calculate a forecast as early as 14 days prior to the premiere date. This research also extends the Diffusion of Innovation theory and diffusion modeling by applying them in the television entertainment environment. Evidence was found supporting Rogers’s (2003) heterophilous communication, also referred to by Granovetter (1973) as “weak ties.” Further, despite a diffusion pattern that differs from other categories, entertainment consumption demonstrates evidence of a mass media (external) channel and an interpersonal eWOM (internal) channel

A multi-modal, multi-platform, and multi-lingual approach to understanding online misinformation

Due to online social media, access to information is becoming easier and easier. Meanwhile, the truthfulness of online information is often not guaranteed. Incorrect information, often called misinformation, can have several modalities, and it can spread to multiple social media platforms in different languages, which can be destructive to society. However, academia and industry do not have automated ways to assess the impact of misinformation on social media, preventing the adoption of productive strategies to curb the prevalence of misinformation. In this dissertation, I present my research to build computational pipelines that help measuring and detecting misinformation on social media. My work can be divided into three parts. The first part focuses on processing misinformation in text form. I first show how to group political news articles from both trustworthy and untrustworthy news outlets into stories. Then I present a measurement analysis on the spread of stories to characterize how mainstream and fringe Web communities influence each other. The second part is related to analyzing image-based misinformation. It can be further divided into two parts: fauxtography and generic image misinformation. Fauxtography is a special type of image misinformation, where images are manipulated or used out-of-context. In this research, I present how to identify fauxtography on social media by using a fact-checking website (Snopes.com), and I also develop a computational pipeline to facilitate the measurement of these images at scale. I next focus on generic misinformation images related to COVID-19. During the pandemic, text misinformation has been studied in many aspects. However, very little research has covered image misinformation during the COVID-19 pandemic. In this research, I develop a technique to cluster visually similar images together, facilitating manual annotation, to make subsequent analysis possible. The last part is about the detection of misinformation in text form following a multi-language perspective. This research aims to detect textual COVID-19 related misinformation and what stances Twitter users have towards such misinformation in both English and Chinese. To achieve this goal, I experiment on several natural language processing (NLP) models to investigate their performance on misinformation detection and stance detection in both monolingual and multi-lingual manners. The results show that two models: COVID-Tweet-BERT v2 and BERTweet are generally effective in detecting misinformation and stance in the two above manners. These two models are promising to be applied to misinformation moderation on social media platforms, which heavily depends on identifying misinformation and stance of the author towards this piece of misinformation. Overall, the results of this dissertation shed light on understanding of online misinformation, and my proposed computational tools are applicable to moderation of social media, potentially benefitting for a more wholesome online ecosystem