Social Media and Presidential Campaigns – Preliminary Results of the 2018 Brazilian Presidential Election

Brito, Kellyton dos Santos; Paula, Natália Cristina Sales de; Fernandes, Manoel do Couto; Meira, Sílvio Romero de Lemos

doi:10.1145/3325112.3325252

Cited by 12 publications

(3 citation statements)

References 27 publications

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“…To achieve this, the study conducted a sentiment analysis on the two major candidates "Donald J. Trump" and "Hillary Clinton" to predict the winner. Kellyton et al [12] identified the correlation between social media and the final outcome of the 2018 Brazilian presidential elections for the thirteen (13) candidates using 291 million social media users" interactions and over 41,000 posts obtained from Instagram, Twitter and Facebook between January 2018 and October 2018. Features of interest adopted include social media profiles, users" posts and number of followers and the posts from the presidential candidates.…”

Section: Related Workmentioning

confidence: 99%

“…However, the desire to predict an election outcome via twitter is to create an alternative to current polls with the minimal cost but still maintaining the standard in terms of precision, accuracy and reliability. Examples of elections forecasted via the use of online social networks include the 2018 Brazilian presidential elections [12], 2016 United States presidential election [13][14][15][16], Indian 2015 election [17], 2014 Brazil presidential election [18], Pakistan Election 2013 [19] and United States presidential election 2012 [20] among others. However, most of these works did not take into cognizance, the presence of software bots in online social networks.…”

Section: Introductionmentioning

confidence: 99%

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Lexicon-based Bot-aware Public Emotion Mining and Sentiment Analysis of the Nigerian 2019 Presidential Election on Twitter

Fagbola¹,

Thakur²

2019

IJACSA

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Online social networks have been widely engaged as rich potential platforms to predict election outcomes' in several countries of the world. The vast amount of readilyavailable data on such platforms, coupled with the emerging power of natural language processing algorithms and tools, have made it possible to mine and generate foresight into the possible directions of elections' outcome. In this paper, lexicon-based public emotion mining and sentiment analysis were conducted to predict win in the 2019 presidential election in Nigeria. 224,500 tweets, associated with the two most prominent political parties in Nigeria, People's Democratic Party (PDP) and All Progressive Congress (APC), and the two most prominent presidential candidates that represented these parties in the 2019 elections, Atiku Abubakar and Muhammadu Buhari, were collected between 9th October 2018 and 17th December 2018 via the Twitter's streaming API. tm and NRC libraries, defined in the 'R' integrated development environment, were used for data cleaning and preprocessing purposes. Botometer was introduced to detect the presence of automated bots in the preprocessed data while NRC Word Emotion Association Lexicon (EmoLex) was used to generate distributions of subjective public sentiments and emotions that surround the Nigerian 2019 presidential election. Emotions were grouped into eight categories (sadness, trust, anger, fear, joy, anticipation, disgust, surprise) while sentiments were grouped into two (negative and positive) based on Plutchik's emotion wheel. Results obtained indicate a higher positive and a lower negative sentiment for APC than was observed with PDP. Similarly, for the presidential aspirants, Atiku has a slightly higher positive and a slightly lower negative sentiment than was observed with Buhari. These results show that APC is the predicted winning party and Atiku as the most preferred winner of the 2019 presidential election. These predictions were corroborated by the actual election results as APC emerged as the winning party while Buhari and Atiku shared very close vote margin in the election. Hence, this research is an indication that twitter data can be appropriately used to predict election outcomes and other offline future events. Future research could investigate spatiotemporal dimensions of the prediction.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Lexicon-based Bot-aware Public Emotion Mining and Sentiment Analysis of the Nigerian 2019 Presidential Election on Twitter

Fagbola¹,

Thakur²

2019

IJACSA

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“…In this new scenario, SM is used extensively in electoral campaigns [1], and an online campaign's success can even decide elections. In practice, recent examples of SM engagement and electoral success include the 2016 U.S. presidential election, when Donald Trump focused his campaign on free-media marketing [2], and the 2018 Brazilian presidential election, when the candidate with more SM engagement but little exposition on traditional media was elected [3]. Moreover, in some way, it is possible to measure how a politician's message is spreading over SM and try to estimate how much attention a candidate is receiving or how many people are talking about a candidate.…”

Section: Introductionmentioning

confidence: 99%

A Systematic Review of Predicting Elections Based on Social Media Data: Research Challenges and Future Directions

Fayaz¹

2021

IJRASET

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The way politicians communicate with the electorateand run electoral campaigns was reshaped by the emergence and popularization of contemporary social media (SM), such as Facebook, Twitter, and Instagram social networks (SN). Due to inherent capabilities of SM, such as the large amount of available data accessed in real time, a new research subject has emerged, focusing on using SM data to predict election outcomes. Despite many studies conducted in the last decade, results are very controversial, and many times challenged. In this context, this work aims to investigate and summarize how research on predicting elections based on SM data has evolved since its beginning, to outline the state of both the art and the practice,and to identify research opportunities within this field. In termsof method, we performed a systematic literature review analyzingthe quantity and quality of publications, the electoral context of studies, the main approaches to and characteristics of the successful studies, as well as their main strengths and challenges, and compared our results with previous reviews. We identified and analyzed 83 relevant studies, and the challenges were identified in many areas such as process, sampling, modeling, performance evaluation and scientific rigor. Main findings include the low success of the most-used approach, namely volume and sentiment analysis on Twitter, and the better results with new approaches, such as regression methods trained with traditional polls. Finally, a vision of future research on integrating advances on process definitions, modeling, and evaluation is also discussed, pointing out, among others, the need for better investigating the application of state-of-art machine learning approaches. Index Terms: Elections, Social Media, Social Networks, Machine Learning, Systematic Review

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A Transfer Learning Analysis of Political Leaning Classification in Cross-domain Content

Caled

Silva

2022

Lecture Notes in Computer Science

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Social Media and Presidential Campaigns – Preliminary Results of the 2018 Brazilian Presidential Election

Cited by 12 publications

References 27 publications

Lexicon-based Bot-aware Public Emotion Mining and Sentiment Analysis of the Nigerian 2019 Presidential Election on Twitter

Lexicon-based Bot-aware Public Emotion Mining and Sentiment Analysis of the Nigerian 2019 Presidential Election on Twitter

A Systematic Review of Predicting Elections Based on Social Media Data: Research Challenges and Future Directions

A Transfer Learning Analysis of Political Leaning Classification in Cross-domain Content

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