Twitter to Manage Emotions in Political Marketing

Monge, María Esther Calderón

doi:10.1080/10496491.2017.1294870

Cited by 11 publications

(4 citation statements)

References 22 publications

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“…This emotional desensitization process applies to the general public opinion, and more specifically to stock markets, but for Trump’s supporters, on the contrary, emotional hyping has succeeded in turning political support into something closer to a personality cult (Reyes, 2020). Unsurprisingly, Twitter has emerged as a powerful political marketing tool for emotionally focused communication which is best exploited by neo-populist movements, which make use of a prevalently negative emotional tone (Calderón-Monge, 2017). Positive emotions may instead be particularly effective in social media mobilization of the electorate in building trust toward institutions (Marquart et al, 2022).…”

Section: Discussionmentioning

confidence: 99%

Commentary—Much ado About Something Else. Donald Trump, the US Stock Market, and the Public Interest Ethics of Social Media Communication

Sacco

Teti

Triveri

2023

International Journal of Business Communication

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We analyze Donald J. Trump’s Twitter activity over the last months of the 2016 presidential campaign, his period as President Elect, and his Presidential term until Fall 2019, shortly before the outbreak of the pandemic. Trump weaponized social networks as a communication tool to build influence on the financial market and the public opinion. We relate Trump’s communication on Twitter to the dynamics of the NASDAQ100 trend over the whole period of study as well as two subperiods, pre-presidential versus presidential. We find that Trump’s hyperactivity on Twitter is followed by a negative market trend, and that tweets covering politically, and economically sensitive topics seem to negatively impact the market, except for real economy-related tweets. Some topics positively received by the market in the pre-presidential phase (e.g., China) become anticipators of negative trading days during the presidential one. We also consider the emotional tone of Trump’s tweets and find an unexpected reversal of the communicative valence of the tweets as to their expected impact on the stock market. Positive sentiment tweets seem to be followed by negative market performance and, maybe more surprisingly, vice versa. It seems that, during the period of observation, the market has learnt to interpret the emotional tone of Trump’s tweets as instrumental to Trump’s political strategy. In particular, the market seems to have realized that negative sentiment in Trump’s communication was entirely functional to political consensus building and not meant to convey market-relevant information. This is at odds with the idea that presidential communication should reflect the public interest, and especially so when it has major implications for the economy. Trump’s use of social media during both his presidential campaign and term questions the principle that institutional responsibility in the digital realm implies treating the infosphere as a commons. We discuss the implications for the functioning of the stock market and the emerging public interest ethical issues related to the breakdown of such principle.

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Section: Discussionmentioning

confidence: 99%

Commentary—Much ado About Something Else. Donald Trump, the US Stock Market, and the Public Interest Ethics of Social Media Communication

Sacco

Teti

Triveri

2023

International Journal of Business Communication

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“…In this context, the study of social networks acquires fundamental importance to understand the use of emotions in this new sphere. Our measurement focuses on monitoring X due to its wide use among certain age brackets and its outstanding activity as a generator and disseminator of political information (Calderón-Monge 2017;Larsson 2020). This paper presents an analysis of the content of the social networks of both political leaders, understood as the people who head the electoral lists of the different political organisations, and the main political parties in the contest.…”

Section: Emotions and Politics: A Storied Journeymentioning

confidence: 99%

Once More, with Feeling! Digital Campaigns and Emotional Candidacies in X in Andalusia and Castilla y León

Pérez-Castaños,

Ruiloba-Núñez

2023

Social Sciences

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In 2022, two regional elections were held that initiated a new electoral cycle in Spain. The first, in February, took place in Castilla y León where, for the first time, the extreme right-wing party, VOX, formed a coalition in a regional government with the right-wing Partido Popular, while, simultaneously, the extreme left-wing party Podemos was barely left with parliamentary representation. The second, in June in Andalusia, led to VOX increasing its representation by two seats and beginning its national level electoral growth; the far-left that was split in two parties, finding that the dynamic that began in Castilla y León also affected them as they lost 10 representatives. This article seeks to compare the emotional strategies of these extremist parties in the 2022 regional elections, focusing especially on leaders’ and parties’ communication strategies on X, formerly Twitter, during the electoral campaign. We expect to find differences between political parties but also between regions. To test this, we carried out an ‘X account content analysis’. We selected those accounts representing the candidates to the regional President and also the main political party accounts in the regions: namely VOX in both regions, Podemos in Castilla y León, and Adelante Andalucía and Por Andalucía in Andalusia.

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“…The detection of one's expectations is also carried out using social media data in the political domain [28]. The data used is data from Twitter.…”

Section: Literature Reviewmentioning

confidence: 99%

Comparison of machine learning for sentiment analysis in detecting anxiety based on social media data

2021

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All groups of people felt the impact of the COVID-19 pandemic. This situation triggers anxiety, which is bad for everyone. The government's role is very influential in solving these problems with its work program. It also has many pros and cons that cause public anxiety. For that, it is necessary to detect anxiety to improve government programs that can increase public expectations. This study applies machine learning to detecting anxiety based on social media comments regarding government programs to deal with this pandemic. This concept will adopt a sentiment analysis in detecting anxiety based on positive and negative comments from netizens. The machine learning methods implemented include K-NN, Bernoulli, Decision Tree Classifier, Support Vector Classifier, Random Forest, and XG-boost. The data sample used is the result of crawling YouTube comments. The data used amounted to 4862 comments consisting of negative and positive data with 3211 and 1651. Negative data identify anxiety, while positive data identifies hope (not anxious). Machine learning is processed based on feature extraction of count-vectorization and TF-IDF. The results showed that the sentiment data amounted to 3889 and 973 in testing, and training with the greatest accuracy was the random forest with feature extraction of vectorization count and TF-IDF of 84.99% and 82.63%, respectively. The best precision test is K-NN, while the best recall is XG-Boost. Thus, Random Forest is the best accurate to detect someone's anxiety based-on data from social media.

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Twitter to Manage Emotions in Political Marketing

Cited by 11 publications

References 22 publications

Commentary—Much ado About Something Else. Donald Trump, the US Stock Market, and the Public Interest Ethics of Social Media Communication

Commentary—Much ado About Something Else. Donald Trump, the US Stock Market, and the Public Interest Ethics of Social Media Communication

Once More, with Feeling! Digital Campaigns and Emotional Candidacies in X in Andalusia and Castilla y León

Comparison of machine learning for sentiment analysis in detecting anxiety based on social media data

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