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:10.2139/ssrn.2603433

Cited by 49 publications

(50 citation statements)

References 19 publications

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“…Previous studies show that Twitter activity may have predictive power for electoral outcomes (e.g., Bermingham and Smeaton, 2011;Tumasjan et al, 2011;Burnap et al, 2016). To explore whether this is the case in our sample, we compare support in Twitter activity and the actual shares of votes received by the sides of the campaigns at the regional level.…”

Section: Predictive Power Of Public Opinions On Twittermentioning

confidence: 95%

Social Media, Sentiment and Public Opinions: Evidence from #Brexit and #USElection

Gorodnichenko

Pham

Talavera

2018

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We are grateful to participants of the 2017 Royal Economics Society Conference, the 12th Annual Conference-Warsaw International Economic Meeting, and the 15th Annual Media Economics Workshop for comments. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research. NBER working papers are circulated for discussion and comment purposes. They have not been peer-reviewed or been subject to the review by the NBER Board of Directors that accompanies official NBER publications.

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Section: Predictive Power Of Public Opinions On Twittermentioning

confidence: 95%

Social Media, Sentiment and Public Opinions: Evidence from #Brexit and #USElection

Gorodnichenko

Pham

Talavera

2018

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“…Twitter data of 2015 UK General Election used by Burnap [14] to forecast the election result. Burnap proposed baseline model that incorporates prior party support and sentiment analysis to generate an accurate forecast of parliament seat allocation.…”

Section: Related Workmentioning

confidence: 99%

Prediction and analysis of Indonesia Presidential election from Twitter using sentiment analysis

2018

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Elections in Indonesia have taken place since 1955 to elect a legislature. At a national level, Indonesian people did not elect a president until 2004. For the first time, the president, and members of People's Consultative Assembly will be elected on the same day [1]. The next general election that will be held in Indonesia is next year on 17 April 2019. Related to this situation, discussion and prediction about who is the Presidential candidate in Indonesia become a hot and interesting conversation among Indonesian citizen, and many of them expressed it through social media. Election-related hashtags are some of the most used hashtags among Indonesian netizens, most of them is a form of support to Jokowi and Prabowo, such as #PilihPrabowo (vote for Prabowo) and #AkhirnyaMili-hJokowi (finally vote for Jokowi) [2]. Political campaigns have exploited this vast array of information available on the above platforms to draw insights about user opinions and thus design their campaign strategy. Huge investments by politicians in social media campaigns right before an election along with arguments and debates between their supporters and opponents only enhance the claim that views and opinions posted by users have a bearing on the results of an election [3]. On the other way, the information

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“…We could only judge 179,494 tweets if we only used sentimental analysis. 7 We could not get a similar result with the final result. The result is different from the final result of this election (shown in Figure 4).…”

Section: Feasibility Justificationmentioning

confidence: 85%

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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140 Characters to Victory?: Using Twitter to Predict the UK 2015 General Election

Cited by 49 publications

References 19 publications

Social Media, Sentiment and Public Opinions: Evidence from #Brexit and #USElection

Social Media, Sentiment and Public Opinions: Evidence from #Brexit and #USElection

Prediction and analysis of Indonesia Presidential election from Twitter using sentiment analysis

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

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