Resonant Moments in Media Events (original) (raw)
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Information , 2024
This study analyzes the linguistic patterns and rhetorical strategies employed in the 2024 U.S. presidential debates from the exchanges between Donald Trump, Joe Biden, and Kamala Harris. This paper examines debate transcripts to find underlying themes and communication styles using Natural Language Processing (NLP) advanced techniques, including an n-gram analysis, sentiment analysis, and lexical diversity measurements. The methodology combines a quantitative text analysis with qualitative interpretation through the Jaccard similarity coefficient, the Type–Token Ratio, and the Measure of Textual Lexical Diversity. The empirical results reveal distinct linguistic profiles for each candidate: Trump consistently employed emotionally charged language with high sentiment volatility, while Biden and Harris demonstrated more measured approaches with higher lexical diversity. Finally, this research contributes to the understanding of political discourse in high-stakes debates through NLP and can offer information on the evolution of the communication strategies of the presidential candidates of any country with this regime.
Of Big Birds and Bayonets: Hybrid Twitter Interactivity in the 2012 Presidential Debates
Information, Communication & Society
The 2012 US Presidential debates were hybrid media events. Millions of viewers “dual-screened” them, simultaneously watching their televisions and commenting on their social media feeds. In doing so, they helped transform verbal gaffes and zingers into debate-defining moments that may have influenced public opinion and media coverage. To examine this phenomenon, we apply network and qualitative textual analyses to a unique dataset of over 1.9 million tweets from the first and third presidential debates. We address two questions of networked information flow within the debate-relevant Twittersphere: who was most responsible for spreading the “Big Bird” and “horses and bayonets” memes, and how did they use humor to discuss it? Our results reveal that nontraditional political actors were prominent network hubs in both debates and that humor was widespread in the first debate and among anti-Romney users.
The 2016 U.S. Presidential Candidates and How People Tweeted About Them
SAGE Open, 2018
The 2016 election provided more language and polling data than any previous election. In addition, the election spurred a new level of social media coverage. The current study analyzed the language of Donald Trump and Hillary Clinton from the debates as well as the tweets of millions of people during the fall presidential campaign. In addition, aggregated polling data allowed for a comparison of daily election-relevant language from Twitter and fluctuations in voter preference. Overall, Trump's debate language was low in analytic/formal thinking and high in negative emotional tone and authenticity. Clinton was high in analytic and positive emotions, low in authenticity. The analysis of almost 10 million tweets revealed that Trumprelevant tweets were generally more positive than Clinton-related tweets. Most important were lag analyses that predicted polling numbers a week later from tweets. In general, when Clinton-related tweets were more analytic, her subsequent poll numbers dropped. Similarly, positive emotion language in the Clinton-related tweets predicted lower poll numbers a week later. Conversely, Trump-related tweets that were high in positive emotion and in analytic thinking predicted higher subsequent polling. In other words, when Twitter language about the candidates was used in ways inconsistent with the candidates themselves, their poll numbers went up. We propose two possible explanations for these findings: the projection hypothesis, a desire to seek qualities the candidates are missing, and the participant hypothesis, a shift in who is participating in the Twitter conversation over the course of the campaigns.
Proceedings of the first SIGMM workshop on Social media - WSM '09, 2009
We investigate the practice of sharing short messages (microblogging) around live media events. Our focus is on Twitter and its usage during the 2008 Presidential Debates. We find that analysis of Twitter usage patterns around this media event can yield significant insights into the semantic structure and content of the media object. Specifically, we find that the level of Twitter activity serves as a predictor of changes in topics in the media event. Further we find that conversational cues can identify the key players in the media object and that the content of the Twitter posts can somewhat reflect the topics of discussion in the media object, but are mostly evaluative, in that they express the poster's reaction to the media.
Characterizing debate performance via aggregated twitter sentiment
Proceedings of the 28th …, 2010
Television broadcasters are beginning to combine social micro-blogging systems such as Twitter with television to create social video experiences around events. We looked at one such event, the first U.S. presidential debate in 2008, in conjunction with aggregated ratings of message sentiment from Twitter. We begin to develop an analytical methodology and visual representations that could help a journalist or public affairs person better understand the temporal dynamics of sentiment in reaction to the debate video. We demonstrate visuals and metrics that can be used to detect sentiment pulse, anomalies in that pulse, and indications of controversial topics that can be used to inform the design of visual analytic systems for social media events.
Communication & Society
The year 2017 was an intense electoral year in Chile, both parliamentary and presidential. In this context, by using computer intelligence, an interdisciplinary team conducted a collection and volumetric analysis of over 3 million Twitter messages belonging to users that mentioned, at least once, any of the presidential candidates, both in the first and second voting round. Our goal was focused on analyzing the relationship between traditional media (radio and television) and Twitter, probing user interactions during the broadcast of live political shows, with emphasis on presidential debates. For this purpose, we carried out a volumetric analysis of all mentions in social media during the broadcast of live political shows to characterize the digital attention of the audience, under different parameters. Our results show that there is high user interest in the digital debate regarding presidential debates, a positive correlation between traditional media and Twitter during the broad...
Using natural language technology to measure Mass media reactions in the election context
This paper presents a computational tool, PEDANT, based on natural language processing (NLP) techniques for the interpretation of the political discourse in the print media. This application considers the 2009 presidential campaign in Romania. The concept behind this method is that the manner in which individuals speak and write, with the aim to deliver a certain image to the public, is an opened window towards their emotional and cognitive worlds. In other words, the vocabulary betrays the author's sensibility. By emphasizing the emotional component at the level of discourse, voters identify with the speaker, who becomes the personification of their common ideals. Our investigation is intended to give support to researchers, specialists in political sciences, to journalists and election's staff, being helpful mainly in their exploration of the political campaigns, in their intend to measure reactions with respect to the developments in the political scene.
Since the US plays a crucial role in global politics, the presidential elections have been closely monitored. One of the ways to inspect the elections is through social media. Twitter is among the most popular social media used to examine public opinion because of the availability of datasets. It is interesting to explore and compare what the US netizens say about the Republican and Democratic presidential nominees for president in the 2020 US election (second debate) on Twitter, as well as the characteristics of users based on their ID names. The dataset used in this study is derived from Tweetsets. The dataset is analyzed under the framework of Social Network Analysis and Discourse Analysis. TF-IDF scores will be employed to extract unique words in each community in the networks. The results show that the relationship of tweeters in @realDonaldTrump network is more extensive, and the network has more active tweeters. According to text analysis, the content in @realDonaldTrump network contains more criticisms and sarcastic messages. The tweets in the network are also more diverse in terms of people who are mentioned, though the usernames in the network are less meaningful and interpretable. @JoeBiden network has almost two times less nodes but is more dense. Although tweet texts in the network also contain criticisms, there are more positive messages as compliments.
Registering the Impact of Words in Spoken Political and Journalistic Texts
Human language, rights, and security, 2021
Words in spoken political and journalistic texts may inspire, infuriate or even become mottos. Often, the entire spoken interaction may be forgotten, yet individual words may remain associated with the Speaker and/or the group represented by the Speaker or even the individual word or words themselves obtain a dynamic of their own, outshining the original Speaker. In the current-state-of affairs, connected with the impact of international news networks and social media, the impact of words in spoken political and journalistic texts is directly linked to its impact to a diverse international audience. The impact or controversy of a word and related topic may be registered by the reaction it generates. Special focus is placed in the registration and evaluation of words and their related topics in spoken political and journalistic discussions and interviews. Although as text types, spoken political and journalistic texts pose challenges for their evaluation, processing and translation, the presented approaches allow the registration of complex and implied information, indications of Speaker's attitude and intentions and can contribute to evaluating the behaviour of Speakers-Participants. This registration also allows the identification of words generating positive, negative or diverse reactions, their relation to Cognitive Bias and their impact to a national and international audience within a context of international news networks and social media.
Tweetgeist: Can the twitter timeline reveal the structure of broadcast events
CSCW Horizons, 2010
We explore applications for enriching experiences around live visual media events by leveraging conversational activity on short messaging services. We investigate the application of existing methods to discover the structure and content of media events and develop methods for exposing the discovered informational cues to users. We demonstrate these approaches using video footage and Twitter activity during two broadly watched media events: the first 2008 USA Presidential Debate and the Inauguration of Barack Obama. For the debates, we demonstrate a method for segmenting and annotating the media via conversational activity for the purpose of watching the video after the event has already passed. For the inauguration, we demonstrate approaches for exposing an awareness of the current topics of discussion on Twitter and the apparent levels of interest via a real-time feedback display. The primary contribution of this CSCW Horizons note is an initial exploration of approaches for applying cues mined from community conversation towards enhanced experiences around multimedia events and an invitation for a discussion of further approaches for applying these techniques to a growing number of domains and applications.