Validation of Twitter opinion trends with national polling aggregates: Hillary Clinton vs Donald Trump

Measuring and forecasting opinion trends from real-time social media is a long-standing goal of big-data analytics. Despite its importance, there has been no conclusive scientific evidence so far that social media activity can capture the opinion of the general population. Here we develop a method to infer the opinion of Twitter users regarding the candidates of the 2016 US Presidential Election by using a combination of statistical physics of complex networks and machine learning based on hashtags co-occurrence to develop an in-domain training set approaching 1 million tweets. We investigate the social networks formed by the interactions among millions of Twitter users and infer the support of each user to the presidential candidates. The resulting Twitter trends follow the New York Times National Polling Average, which represents an aggregate of hundreds of independent traditional polls, with remarkable accuracy. Moreover, the Twitter opinion trend precedes the aggregated NYT polls by 10 days, showing that Twitter can be an early signal of global opinion trends. Our analytics unleash the power of Twitter to uncover social trends from elections, brands to political movements, and at a fraction of the cost of national polls

Twigraph: Discovering and Visualizing Influential Words between Twitter Profiles

The social media craze is on an ever increasing spree, and people are connected with each other like never before, but these vast connections are visually unexplored. We propose a methodology Twigraph to explore the connections between persons using their Twitter profiles. First, we propose a hybrid approach of recommending social media profiles, articles, and advertisements to a user.The profiles are recommended based on the similarity score between the user profile, and profile under evaluation. The similarity between a set of profiles is investigated by finding the top influential words thus causing a high similarity through an Influence Term Metric for each word. Then, we group profiles of various domains such as politics, sports, and entertainment based on the similarity score through a novel clustering algorithm. The connectivity between profiles is envisaged using word graphs that help in finding the words that connect a set of profiles and the profiles that are connected to a word. Finally, we analyze the top influential words over a set of profiles through clustering by finding the similarity of that profiles enabling to break down a Twitter profile with a lot of followers to fine level word connections using word graphs. The proposed method was implemented on datasets comprising 1.1 M Tweets obtained from Twitter. Experimental results show that the resultant influential words were highly representative of the relationship between two profiles or a set of profile

Predicting the Outcomes of Important Events based on Social Media and Social Network Analysis

Twitter is a famous social network website that lets users post their opinions about current affairs, share their social events, and interact with others. It has now become one of the largest sources of news, with over 200 million active users monthly. It is possible to predict the outcomes of events based on social networks using machine learning and big data analytics. Massive data available from social networks can be utilized to improve prediction efficacy and accuracy. It is a challenging problem to achieve high accuracy in predicting the outcomes of political events using Twitter data. The focus of this thesis is to investigate novel approaches to predicting the outcomes of political events from social media and social networks. The first proposed method is to predict election results based on Twitter data analysis. The method extracts and analyses sentimental information from microblogs to predict the popularity of candidates. Experimental results have shown its advantages over the existing method for predicting outcomes of politic events. The second proposed method is to predict election results based on Twitter data analysis that analyses sentimental information using term weighting and selection to predict the popularity of candidates. Scaling factors are used for different types of terms, which help to select informative terms more effectively and achieve better prediction results than the previous method. The third method proposed in this thesis represents the social network by using network connectivity constructed based on retweet data and social media contents as well, leading to a new approach to predicting the outcome of political events. Two approaches, whole-network and sub-network, have been developed and compared. Experimental results show that the sub-network approach, which constructs sub-networks based on different topics, outperformed the whole-network approach

Routledge Handbook of Autocratization in South Asia

This handbook offers a comprehensive analysis of the processes and actors contributing to autocratization in South Asia. It provides an enhanced understanding of the interconnectedness of the different states in the region, and how that may be related to autocratization. The book analyzes issues of state power, the support for political parties, questions relating to economic actors and sustainable economic development, the role of civil society, questions of equality and political culture, political mobilization, the role of education and the media, as well as topical issues such as the Covid pandemic, environmental issues, migration, and military and international security. Structured in five sections, contributions by international experts describe and explain outcomes at the national level in India, Pakistan, Bangladesh and Sri Lanka. The final section analyzes conditions for democracy and autocratization and how they are affected by the interplay of political forces at the international level in this region. India – building an ethnic state? Pakistan – the decline of civil liberties Bangladesh – towards one-party rule Sri Lanka – the resilience of the ethnic state How to comprehend autocratization in South Asia – three broad perspectives This innovative handbook is the first to describe and to explain ongoing trends of autocratization in South Asia, demonstrating that drivers of political change also work across boundaries. It is an important reference work for students and researchers of South Asian Studies, Asian Studies, Area Studies and Political Science.　 The Open Access version of this book, available at http://www.taylorfrancis.com, has been made available under a Creative Commons Attribution-Non Commercial-No Derivatives 4.0 license

Digital activism in Asia reader

The digital turn might as well be marked as an Asian turn. From flash-mobs in Taiwan to feminist mobilisations in India, from hybrid media strategies of Syrian activists to cultural protests in Thailand, we see the emergence of political acts that transform the citizen from being a beneficiary of change to becoming an agent of change. In co-shaping these changes, what the digital shall be used for, and what its consequences will be, are both up for speculation and negotiation. Digital Activism in Asia marks a particular shift where these questions are no longer being refracted through the ICT4D logic, or the West’s attempts to save Asia from itself, but shaped by multiplicity, unevenness, and urgencies of digital sites and users in Asia. This reader crowd-sources critical tools, concepts, analyses, and annotations, self-identified by a network of change makers in Asia as important in their own practices within their own contexts