Measuring relative opinion from location-based social media: A case study of the 2016 U.S. presidential election

Social media has become an emerging alternative to opinion polls for public opinion collection, while it is still posing many challenges as a passive data source, such as structurelessness, quantifiability, and representativeness. Social media data with geotags provide new opportunities to unveil the geographic locations of users expressing their opinions. This paper aims to answer two questions: 1) whether quantifiable measurement of public opinion can be obtained from social media and 2) whether it can produce better or complementary measures compared to opinion polls. This research proposes a novel approach to measure the relative opinion of Twitter users towards public issues in order to accommodate more complex opinion structures and take advantage of the geography pertaining to the public issues. To ensure that this new measure is technically feasible, a modeling framework is developed including building a training dataset by adopting a state-of-the-art approach and devising a new deep learning method called Opinion-Oriented Word Embedding. With a case study of the tweets selected for the 2016 U.S. presidential election, we demonstrate the predictive superiority of our relative opinion approach and we show how it can aid visual analytics and support opinion predictions. Although the relative opinion measure is proved to be more robust compared to polling, our study also suggests that the former can advantageously complement the later in opinion prediction

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

Political opinion dynamics in social networks: The Portuguese 2010-11: Case study

The research on opinion dynamics in social networks and opinion influence models often suffer from a lack of grounding in social theories as well as deficient empirical data validation. The current availability of large datasets, and the ease we can now collect social data from the Internet, makes validation of theoretical social models a less difficult task. Starting by a state-of-the-art of the research and practice concerning political opinion dynamics in social networks, we identify the main strengths and weaknesses of this domain. We then propose a novel method for uncovering political opinion dynamics using on-line data gathering. The method includes three distinct phases: (1) data collection, (2) multi-agent modelling (3) validation. Specifically, we tested the significance of both Social Impact Theory, originally proposed by Latané (1981), and Brownian Agent modelling, proposed by Schweitzer (2002), for characterizing political opinion formation during electoral periods. These two models were tested using more than 100.000 tweets collected during the periods from the 30th of October to the 21st of January 2011 and from the 27th of March to the 6th of June 2011, concerning the Portuguese presidential and legislative elections occurred in 2011. Following the data collection, two distinct on-line communities were inspected: the general Twitter user community, and the traditional news media Twitter feeds. The opinion dynamics was simulated with grid adjustment of model parameters. This operation was performed on separate empirical series, respecting the talk about the six electoral candidates and parties. The complete process allowed concluding about the explanatory power of Social Impact Theory and Brownian Agents, and, on the other side, allowed characterizing opinion dynamics in this specific case study. This article details each phase of the method, illustrated using the dataset available at http://work.theobservatorium.eu/presid2011.info:eu-repo/semantics/acceptedVersio