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

Xiangyu Hu, Lemin Li, Tingmin Wu, Xiaoxiang Ai, Jie Gu, Sheng Wen

Research output: Contribution to journalArticleResearchpeer-review

1 Citation (Scopus)

Abstract

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.

Original languageEnglish
Article numbere4525
Number of pages11
JournalConcurrency and Computation-Practice & Experience
Volume31
Issue number21
DOIs
Publication statusPublished - 27 Jul 2019
Externally publishedYes

Keywords

  • negative information influence
  • social media

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