Social Media Would Not Lie: Prediction of the 2016 Taiwan Election via Online Heterogeneous Data

The prevalence of online media has attracted researchers from various domains to explore human behavior and make interesting predictions. In this research, we leverage heterogeneous social media data collected from various online platforms to predict Taiwan's 2016 presidential election. In contrast to most existing research, we take a "signal" view of heterogeneous information and adopt the Kalman filter to fuse multiple signals into daily vote predictions for the candidates. We also consider events that influenced the election in a quantitative manner based on the so-called event study model that originated in the field of financial research. We obtained the following interesting findings. First, public opinions in online media dominate traditional polls in Taiwan election prediction in terms of both predictive power and timeliness. But offline polls can still function on alleviating the sample bias of online opinions. Second, although online signals converge as election day approaches, the simple Facebook "Like" is consistently the strongest indicator of the election result. Third, most influential events have a strong connection to cross-strait relations, and the Chou Tzu-yu flag incident followed by the apology video one day before the election increased the vote share of Tsai Ing-Wen by 3.66%. This research justifies the predictive power of online media in politics and the advantages of information fusion. The combined use of the Kalman filter and the event study method contributes to the data-driven political analytics paradigm for both prediction and attribution purposes

Social Media and Electoral Predictions: A Meta-Analytic Review

Can social media data be used to make reasonably accurate estimates of electoral outcomes? We conducted a meta-analytic review to examine the predictive performance of different features of social media posts and different methods in predicting political elections: (1) content features; and (2) structural features. Across 45 published studies, we find significant variance in the quality of predictions, which on average still lag behind those in traditional survey research. More specifically, our findings that machine learning-based approaches generally outperform lexicon-based analyses, while combining structural and content features yields most accurate predictions

Lexicon-based bot-aware public emotion mining and sentiment analysis of the Nigerian 2019 presidential election on Twitter

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 readily-available 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

Prediction of U.S. Election Using Twitter Data: A Case Study

Social network/media has become popular over the last few years and is moving closer to be an integral part in one�s life. With the rise of new social media movement, the analysis of social networking blog contents has become an important tool of big data analytics. Recent research studies on the use of Twitter for predicting political elections have raised many questions as well as interest in using Twitter data for predictive analysis. The overarching objective of our research is to study the capability of Twitter data as an ex-ante indicator of event outcomes. The 2014 US midterm election has been chosen as the event for this study. This work analyses both pre-poll and post-poll data from Twitter related to 2014 midterm elections in U.S. Relevant tweets are extracted from the tweet stream with the help of a Map-Reduce Program in a Hadoop system by specifying appropriate keywords configuration for running Apache Flume. This data are classified into four groups using �Democrat� and �Republican� as the division criteria. Two time-series of sentiments (positive and negative) are constructed for each group. Several statistics are also compiled from each group of tweets and used as predictive indicators. Original tweet count, retweet count, and user count in each group are some of the statistics compiled. All the statistics favor the Republican party to win which actually was the outcome of the election. Our research consists of two parts. The first part is prediction of election results and the second part is modeling sentiment before and after the election. We used Hidden Markov Model as a tool for both parts. The hidden states of the model were used as sentiment indicators and state changes were interpreted as sentiment changes. The results of the HMM agreed with the actual outcomes. Our study provides support for the argument that Twitter data can be considered as a reliable predictor of events.Computer Scienc