Dataset Information

Social media popularity and election results: A study of the 2016 Taiwanese general election.

ABSTRACT: This paper investigates the relationship between candidates' online popularity and election results, as a step towards creating a model to forecast the results of Taiwanese elections even in the absence of reliable opinion polls on a district-by-district level. 253 of 354 legislative candidates of single-member districts in Taiwan's 2016 general election had active public Facebook pages during the election period. Hypothesizing that the relative popularity of candidates' Facebook posts will be positively related to their election results, I calculated each candidate's Like Ratio (i.e. proportions of all likes on Facebook posts obtained by candidates in their district). In order to have a measure of online interest without the influence of subjective positivity, I similarly calculated the proportion of daily average page views for each candidate's Wikipedia page. I ran a regression analysis, incorporating data on results of previous elections and available opinion poll data. I found the models could describe the result of the election well and reject the null hypothesis. My models successfully predicted 80% of winners in single-member districts and were effective in districts without local opinion polls with a predictive power approaching that of traditional opinion polls. The models also showed good accuracy when run on data for the 2014 Taiwanese municipal mayors election.

SUBMITTER: Zhang X

PROVIDER: S-EPMC6261632 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Social media popularity and election results: A study of the 2016 Taiwanese general election.

Zhang Xiaodong X

PloS one 20181128 11

This paper investigates the relationship between candidates' online popularity and election results, as a step towards creating a model to forecast the results of Taiwanese elections even in the absence of reliable opinion polls on a district-by-district level. 253 of 354 legislative candidates of single-member districts in Taiwan's 2016 general election had active public Facebook pages during the election period. Hypothesizing that the relative popularity of candidates' Facebook posts will be p ...[more]

PMID: 30485347

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Project description:COVID-19 originally known as Corona VIrus Disease of 2019, has been declared as a pandemic by World Health Organization (WHO) on 11th March 2020. Unprecedented pressures have mounted on each country to make compelling requisites for controlling the population by assessing the cases and properly utilizing available resources. The rapid number of exponential cases globally has become the apprehension of panic, fear and anxiety among people. The mental and physical health of the global population is found to be directly proportional to this pandemic disease. The current situation has reported more than twenty four million people being tested positive worldwide as of 27th August, 2020. Therefore, it is the need of the hour to implement different measures to safeguard the countries by demystifying the pertinent facts and information. This paper aims to bring out the fact that tweets containing all handles related to COVID-19 and WHO have been unsuccessful in guiding people around this pandemic outbreak appositely. This study analyzes two types of tweets gathered during the pandemic times. In one case, around twenty three thousand most re-tweeted tweets within the time span from 1st Jan 2019 to 23rd March 2020 have been analyzed and observation says that the maximum number of the tweets portrays neutral or negative sentiments. On the other hand, a dataset containing 226,668 tweets collected within the time span between December 2019 and May 2020 have been analyzed which contrastingly show that there were a maximum number of positive and neutral tweets tweeted by netizens. The research demonstrates that though people have tweeted mostly positive regarding COVID-19, yet netizens were busy engrossed in re-tweeting the negative tweets and that no useful words could be found in WordCloud or computations using word frequency in tweets. The claims have been validated through a proposed model using deep learning classifiers with admissible accuracy up to 81%. Apart from these the authors have proposed the implementation of a Gaussian membership function based fuzzy rule base to correctly identify sentiments from tweets. The accuracy for the said model yields up to a permissible rate of 79%.

Project description:BackgroundTardive dyskinesia (TD) is a persistent and potentially disabling movement disorder associated with prolonged exposure to dopamine receptor blocking agents such as antipsychotics. With the expanding use of antipsychotics, research is needed to better understand patient perspectives of TD, which clinical assessments may fail to capture. Social media listening (SML), which is recognized by the US FDA as a method that can advance ongoing efforts for more patient-focused drug development, has been used to understand patient experiences in other disease states. This is the first study to use SML analysis of unsolicited patient and caregiver insights to help clinicians understand how patients describe their symptoms, the emotional distress associated with TD, and the impact on caregivers.MethodsIn this pilot study, a comprehensive search was performed for publicly available, English-language, online content posted between March 2017 and November 2019 on social media platforms, blogs, and forums. An analytics platform (NetBase™) identified posts containing patient or caregiver experiences of assumed TD using predefined search terms. All posts were manually curated and reviewed to ensure quality and validity of the post and to further classify key symptoms, sentiments, and themes.ResultsA total of 261 posts from patients/caregivers ("patient insights") were identified using predefined search terms; 107 posts were used for these analyses. Posts were primarily from forums (47%) and Twitter (33%). Analysis of the most common sentiment-related terms (e.g. "feel" [n = 31], "worse" [n = 17], "symptom" [n = 14], "better" [n = 12]) indicated that 64% were negative, 33% were neutral, and 3% were positive. Theme analysis revealed that patients often felt angry about having TD from a medication used to treat a different condition. In addition, patients felt insecure, including feeling unaccepted by society and fear of being judged by others.ConclusionAlthough this study was limited by inherent methodological constraints (e.g., small sample size, reliance on patient self-report), the perspectives generated from analyzing social media may help convey the unmet needs of patients with TD. This analysis indicated that movement-related symptoms are the most common patient concern, resulting in strong feelings of anger and insecurity.

Dataset Information

Social media popularity and election results: A study of the 2016 Taiwanese general election.

Publications

Social media popularity and election results: A study of the 2016 Taiwanese general election.

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