Quantising opinions for political tweets analysis

There have been increasing interests in recent years in analyzing tweet messages relevant to political events so as to understand public opinions towards certain political issues. We analyzed tweet messages crawled during the eight weeks leading to the UK General Election in May 2010 and found that activities at Twitter is not necessarily a good predictor of popularity of political parties. We then proceed to propose a statistical model for sentiment detection with side information such as emoticons and hash tags implying tweet polarities being incorporated. Our results show that sentiment analysis based on a simple keyword matching against a sentiment lexicon or a supervised classifier trained with distant supervision does not correlate well with the actual election results. However, using our proposed statistical model for sentiment analysis, we were able to map the public opinion in Twitter with the actual offline sentiment in real world

Social media analytics and the role of twitter in the 2014 South Africa general election: a case study

A dissertation submitted to the Faculty of Science, University of the Witwatersrand, Johannesburg, in fulfilment of the requirements for the degree of Master of Science., University of the Witwatersrand, Johannesburg, 2018Social network sites such as Twitter have created vibrant and diverse communities in which users express their opinions and views on a variety of topics such as politics. Extensive research has been conducted in countries such as Ireland, Germany and the United States, in which text mining techniques have been used to obtain information from politically oriented tweets. The purpose of this research was to determine if text mining techniques can be used to uncover meaningful information from a corpus of political tweets collected during the 2014 South African General Election. The Twitter Application Programming Interface was used to collect tweets that were related to the three major political parties in South Africa, namely: the African National Congress (ANC), the Democratic Alliance (DA) and the Economic Freedom Fighters (EFF). The text mining techniques used in this research are: sentiment analysis, clustering, association rule mining and word cloud analysis. In addition, a correlation analysis was performed to determine if there exists a relationship between the total number of tweets mentioning a political party and the total number of votes obtained by that party. The VADER (Valence Aware Dictionary for sEntiment Reasoning) sentiment classifier was used to determine the public’s sentiment towards the three main political parties. This revealed an overwhelming neutral sentiment of the public towards the ANC, DA and EFF. The result produced by the VADER sentiment classifier was significantly greater than any of the baselines in this research. The K-Means cluster algorithm was used to successfully cluster the corpus of political tweets into political-party clusters. Clusters containing tweets relating to the ANC and EFF were formed. However, tweets relating to the DA were scattered across multiple clusters. A fairly strong relationship was discovered between the number of positive tweets that mention the ANC and the number of votes the ANC received in election. Due to the lack of data, no conclusions could be made for the DA or the EFF. The apriori algorithm uncovered numerous association rules, some of which were found to be interest- ing. The results have also demonstrated the usefulness of word cloud analysis in providing easy-to-understand information from the tweet corpus used in this study. This research has highlighted the many ways in which text mining techniques can be used to obtain meaningful information from a corpus of political tweets. This case study can be seen as a contribution to a research effort that seeks to unlock the information contained in textual data from social network sites.MT 201

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

Freedom to Tweet or Tweet to Freedom: The Relationship between Freedom Status and Tweets during Elections

In this thesis, I conduct an exploratory study of the relationship between a country's freedom and the twitter activity during elections. While there have been many studies of Twitter and elections, there has been no previous research conducted to explore the relationship between a countries' freedom and how Twitter influences elections in that given country. My goal is to identify hypotheses for future work in this area, introduce research designs and to shed light on areas of research where there seems to be little indication of relationships. I explore this space with automated analysis of the tweets' text, election outcomes, freedom ratings for the countries, and sentiment analysis. My results show that there seems to be a weak relationship between the outcome of an election and the sentiment expressed towards a candidate in tweets and that there is no relationship between the freedom in a given country and the sentiment expressed towards the incumbent. I found promising initial results regarding the relationship among content removed from links during an election and freedom status of a country, as well as the correlation between how frequently a candidate is mentioned and the election outcome. In the discussion, I present research questions in areas that are promising for future work

Political Journalists Tweet About the Final 2016 Presidential Debate

Past research shows that journalists are gatekeepers to information the public seeks. Using the gatekeeping and agenda-setting theory, this study used a content analysis of tweets from political journalists during the final 2016 presidential debate to examine social media usage in efforts to convey information to followers and whether social media has allowed for journalists to present a more transparent view of candidates to the public. This study used feminist political theory to further analyze whether the tweets from political journalists portrayed Hillary Clinton, the female candidate, with stereotypical “female” traits, such as more emotional and more trustworthy. Applying these theories, this study found that political journalists use social media for personal uses and when discussing politics are still gatekeepers of information. When the debates were discussed, the study demonstrates there was little discussion via tweets of gendered traits and issues in regards to Hillary Clinton and Donald Trump

Prediction Markets, Social Media and Information Efficiency

We consider the impact of breaking news on market prices. We measure activity on the micro-blogging platform Twitter surrounding a unique, newsworthy and identifiable event and investigate subsequent movements of betting prices on the prominent betting exchange, Bet- fair. The event we use is the Bigotgate scandal, which occurred during the 2010 UK General Election campaign. We use recent developments in time series econometric methods to identify and quantify movements in both Twitter activity and Betfair prices, and compare the timings of the two. We find that the response of market prices appears somewhat sluggish and is indicative of market inefficiency, as Betfair prices adjust with a delay, and there is evidence for post-news drift. This slow movement may be explained by the need for corroborating evidence via more traditional forms of media. Once important Tweeters begin to Tweet, including importantly breaking news Twitter feeds from traditional media sources, prices begin to move

Midterm 2018 and targeting Latino community through misinformation and disinformation online

How has the Latino community been targeted by digital propaganda during the 2018 midterm elections in the US? How might this affect their involvement in and around the election? This thesis starts to answer these questions through a data analysis on two of the most prominent and popular social media platforms for political discussion: Twitter and Reddit. This study analyzed people’s posting patterns over time, the digital traces of the individuals pushing the majority and most popular content, and Latino candidates’ interaction on Twitter. This research provides evidence that on Twitter there are two main actors discussing Latinos and politics: “Pro-Latino NGOs” and “Pro-Trump one hit wonders.” The Non-Governmental Organizations (NGOs) discussed political events sporadically and focused more on helping Latinos who had been long-term in the US to obtain better work opportunities, as well as registering to vote. The one-hit wonders were famous people who posted one tweet highlighting the positive things US president Trump has done with Latinos and received massive attention from audiences (in terms of retweets and likes). On Reddit, it was identified that there were a mix of highly active people: (1) individuals posting about president Trump being racist against Latinos; (2) people who simply posted news stories about Latinos and politics; and (3) political trolls focused on mobilizing people to vote Republican (pro-Trump) in the elections. The political trolls shared stories of how Latinos across the US were also supporting Trump and how Trump’s policies against illegal immigrants from Latin America was beneficial to the US. These trolls also appeared to coordinate with high profile people to gain mass visibility outside Reddit for their cause. As the Latino voters, the second largest population group in the US, prepared to head to the polls, humanizing the effects of disinformation against Latinos becomes crucial. Most political actors are not mentioning or organizing Latinos on Twitter. The political organizations, specifically NGOs, that do focus on Latinos rarely discuss politics and focus more on helping Latinos to thrive in the US. Hence, this research is currently highlighting that there is a gap between Latinos and the US midterm elections. It appears that it is more the extremist voices, such as political trolls, who are engaging Latinos for months before the election. In the data we analyzed, there currently does not appear to be any group that is actively mobilizing Latinos to vote without falling into extreme behaviors. We finish by discussing recommendations to counterattack and diminish the effects of disinformation targeting the Latino community and increase their involvement in future US election by use of a civic tech by NGOs

Yes scotland versus better together: how did it all happen?

On the 18th of September 2014, Scottish voters have rejected political independence by a margin of 55,3% against 44,7%. Yet during more than 16 weeks, two opposing campaigns - Yes Scotland versus Better Together - have strived to convince Scotland that political independence versus keeping the Union was the best choice for Scotland’s future. Filled with many unexpected moments, the campaign was intense, vibrant and almost breathtaking. The purpose of this article is to deliver a coherent and consistent account of the Scottish campaigns in order to make sense of the “no” vote. In this article, we will proceed in four moments: first, we will put the referendum in context; second, we will highlight major aspects of the campaigns; third, we will bring the political process up to date and will clarify the terms of the agreement reached under the Smith Process. Finally, in the last part, we will summarize the lessons to learn from the political outcome of the referendum