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

Continuous Theta Burst Stimulation of the Posterior Medial Frontal Cortex to Experimentally Reduce Ideological Threat Responses.

Decades of behavioral science research have documented functional shifts in attitudes and ideological adherence in response to various challenges, but little work to date has illuminated the neural mechanisms underlying these dynamics. This paper describes how continuous theta burst transcranial magnetic stimulation may be employed to experimentally assess the causal contribution of cortical regions to threat-related ideological shifts. In the example protocol provided here, participants are exposed to a threat prime-an explicit reminder of their own inevitable death and bodily decomposition-following a downregulation of the posterior medial frontal cortex (pMFC) or a sham stimulation. Next, disguised within a series of distracter tasks, participants' relative degree of ideological adherence is assessed-in the present example, with regard to coalitional prejudice and religious belief. Participants for whom the pMFC has been downregulated exhibit less coalitionally biased responses to an immigrant critical of the participants' national in-group, and less conviction in positive afterlife beliefs (i.e., God, angels, and heaven), despite having recently been reminded of death. These results complement prior findings that continuous theta burst stimulation of the pMFC influences social conformity and sharing and illustrate the feasibility of investigating the neural basis of high-level social cognitive shifts using transcranial magnetic stimulation

Opinion Polarization by Learning from Social Feedback

We explore a new mechanism to explain polarization phenomena in opinion dynamics in which agents evaluate alternative views on the basis of the social feedback obtained on expressing them. High support of the favored opinion in the social environment, is treated as a positive feedback which reinforces the value associated to this opinion. In connected networks of sufficiently high modularity, different groups of agents can form strong convictions of competing opinions. Linking the social feedback process to standard equilibrium concepts we analytically characterize sufficient conditions for the stability of bi-polarization. While previous models have emphasized the polarization effects of deliberative argument-based communication, our model highlights an affective experience-based route to polarization, without assumptions about negative influence or bounded confidence.Comment: Presented at the Social Simulation Conference (Dublin 2017

On Measuring Bias in Online Information

Bias in online information has recently become a pressing issue, with search engines, social networks and recommendation services being accused of exhibiting some form of bias. In this vision paper, we make the case for a systematic approach towards measuring bias. To this end, we discuss formal measures for quantifying the various types of bias, we outline the system components necessary for realizing them, and we highlight the related research challenges and open problems.Comment: 6 pages, 1 figur