140 characters to victory?: Using Twitter to predict the UK 2015 General Election

Burnap, Pete; Gibson, Rachel; Sloan, Luke; Southern, Rosalynd; Williams, Matthew Leighton

doi:10.1016/j.electstud.2015.11.017

Cited by 152 publications

(74 citation statements)

References 16 publications

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“…This result also reflected in the prediction in 2015 British election. 7 We could not get a similar result with the final result. The reason was that most supporters of labour were young people who often commented on the Internet.…”

Section: Feasibility Justificationmentioning

confidence: 85%

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Every word is valuable: Studied influence of negative words that spread during election period in social media

et al. 2018

Concurrency and Computation

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Studying the influence of negative words that spread during election period is an important work in social media. Most of current methods rely on sentiment analysis of tweets to determine the users' preference. However, sentiment analysis can only makes use of emotional words (ie, adverbs and adjectives), which only take 30 percent of the context in the Internet. According to our empirical analysis based on real datasets, the bias on word selection largely reduced the accuracy of the context in the Internet. In order to address this critical problem, we propose a new method that makes use of nouns with emotional context to determine the election preference of each user. By collecting the frequencies of words in context, we weigh the impact of each supportive/objective noun to strengthen the determination of users' preference. Final results will further be integrated to examine the effectiveness and efficiency of our proposed method. To indicate this idea, we collect and adopt real datasets (UK Prime Minister 2017 and US President Campaign 2016) in the experiments. All the experiment results suggested that our integrated method largely outperformed previous prediction methods. In particular, the prediction results were quite similar to the final results of the UK and US election. Meanwhile, for UK election, we found that the daily approval rate is closely related to the event happened everyday. KEYWORDS negative information influence, social media 1 Concurrency Computat Pract Exper. 2019;31:e4525.wileyonlinelibrary.com/journal/cpe

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Section: Feasibility Justificationmentioning

confidence: 85%

“…In 2015, P. Burnap used sentimental analysis in Britain election. 7 They predicted that the labour party would win, but the final result was on the opposite side. They gave the reason that most supporters of labour party were young people who were more likely to show their ideas on the Internet.…”

Section: Inaccuracy Caused By Tweet Coverage Problemmentioning

confidence: 99%

Every word is valuable: Studied influence of negative words that spread during election period in social media

et al. 2018

Concurrency and Computation

View full text Add to dashboard Cite

show abstract

“…First, computational analysis of the sentiment and the public mood is faster, more precise, and less costly than conducting large-scale surveys. Second, there is strong support for this claim that the sentiment obtained from this approach is a valid indicator of public opinion, as far as it is used to predict many socio-economic phenomena, such as presidential elections (Burnap, Gibson, Sloan, Southern, & Williams, 2016;Tumasjan, Sprenger, Sandner, & Welp, 2010;White, 2016) and commercial sales (Choi & Varian, 2012;Liu, Ding, Chen, Chen, & Guo, 2016;Mishne & Glance, 2006).…”

Section: Related Workmentioning

confidence: 98%

Tehran stock exchange prediction using sentiment analysis of online textual opinions

Ghahfarrokhi

Shamsfard

2020

Intell Sys Acc Fin Mgmt

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Summary We investigate the impact of social media data in predicting the Tehran Stock Exchange variables for the first time. We consider the closing price and daily return of three different stocks for this investigation. We collected our social media data from http://Sahamyab.com/stocktwits for about 3 months. To extract information from online comments, we propose a hybrid sentiment analysis approach that combines lexicon‐based and learning‐based methods. Since lexicons that are available for the Persian language are not practical for sentiment analysis in the stock market domain, we built a particular sentiment lexicon for this domain. After designing and calculating daily sentiment indices using the sentiment of the comments, we examine their impact on the baseline models that only use historical market data and propose new predictor models using multi‐regression analysis. In addition to the sentiments, we also examine the comments volume and the users' reliabilities. We conclude that the predictability of various stocks in the Tehran Stock Exchange is different depending on their attributes. Moreover, we indicate that only comments volume could be useful for predicting the closing price, and both the volume and the sentiment of the comments could be useful for predicting the daily return. We demonstrate that users' trust coefficients have different behaviours toward the three stocks.

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“…Burnap et al [40] not only included a sentiment analysis but also details pertaining to users' prior party support to predict the outcome of the 2015 UK general election. Having trained a model using nearly 14 million tweets and relying on a range from extremely negative to extremely positive to describe users' sentiments, the researchers predicted that the Labour Party would win the election.…”

Section: Literature Reviewmentioning

confidence: 99%

Measuring the Diversity of Facebook Reactions to Research

Freeman

Alhoori

Shahzad

2020

Proc. ACM Hum.-Comput. Interact.

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Online and in the real world, communities are bonded together by emotional consensus around core issues. Emotional responses to scientific findings often play a pivotal role in these core issues. When there is too much diversity of opinion on topics of science, emotions flare up and give rise to conflict. This conflict threatens positive outcomes for research. Emotions have the power to shape how people process new information. They can color the public's understanding of science, motivate policy positions, even change lives. And yet little work has been done to evaluate the public's emotional response to science using quantitative methods. In this paper, we use a dataset of responses to scholarly articles on Facebook to analyze the dynamics of emotional valence, intensity, and diversity. We present a novel way of weighting click-based reactions that increases their comprehensibility, and use these weighted reactions to develop new metrics of aggregate emotional responses. We use our metrics along with LDA topic models and statistical testing to investigate how users' emotional responses differ from one scientific topic to another. We find that research articles related to gender, genetics, or agricultural/environmental sciences elicit significantly different emotional responses from users than other research topics. We also find that there is generally a positive response to scientific research on Facebook, and that articles generating a positive emotional response are more likely to be widely shared-a conclusion that contradicts previous studies of other social media platforms.

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140 characters to victory?: Using Twitter to predict the UK 2015 General Election

Cited by 152 publications

References 16 publications

Every word is valuable: Studied influence of negative words that spread during election period in social media

Every word is valuable: Studied influence of negative words that spread during election period in social media

Tehran stock exchange prediction using sentiment analysis of online textual opinions

Measuring the Diversity of Facebook Reactions to Research

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