Using Machine Learning to Measure Political Polarization on Social Media

Polarization in the political sphere, seen through combative communication and stalemate, may impose negative social impacts on the population. Attempting to measure political polarization in the masses through self-reported surveys and interviews can present response biases of social desirability. The classification of thought freely written online allows political polarization to be measured in an impartial manner. Reddit is one application that enables users to share opinions and create discussions anonymously; this text can be used to measure the political climate at any given time. Disagreement has grown over the perceived level of polarization in our society. The purpose of my research is to measure the degree of political polarization over time by collecting and classifying threads of dialogue within political communities on Reddit. I utilized Reddit APIs to gather threads of text from multiple subreddit communities online. I recruited a team of evaluators to hand-annotate the threads for polarization. Then, I created a machine learning classifier to predict whether a thread posted on Reddit is polarized or not. I trained a multilayer perceptron on the hand-tagged data. Depending on parameter choices, the classifier performed with an accuracy of between 75% and 80% on an independent test set. There does not appear to be a strong pattern indicating a rise in polarization overall on Reddit during the period 2008 to the present. However, when looking at some of the subreddits individually, there is evidence of an increase in polarization

Multidimensional Tie Strength and Economic Development

The strength of social relations has been shown to affect an individual’s access to opportunities. To date, however, the correspondence between tie strength and population’s economic prospects has not been quantified, largely because of the inability to operationalise strength based on Granovetter’s classic theory. Our work departed from the premise that tie strength is a unidimensional construct (typically operationalized with frequency or volume of contact), and used instead a validated model of ten fundamental dimensions of social relationships grounded in the literature of social psychology. We built state-of-the-art NLP tools to infer the presence of these dimensions from textual communication, and analyzed a large conversation network of 630K geo-referenced Reddit users across the entire US connected by 12.8M social ties created over the span of 7 years. We found that unidimensional tie strength is only weakly correlated with economic opportunities ([Formula: see text] ), while multidimensional constructs are highly correlated ([Formula: see text] ). In particular, economic opportunities are associated to the combination of: (i) knowledge ties, which bridge geographically distant groups, facilitating the knowledge dissemination across communities; and (ii) social support ties, which knit geographically close communities together, and represent dependable sources of social and emotional support. These results point to the importance of developing high-quality measures of tie strength in network theory

The Law of the Jungle. The Online Hate-speech against the Roma in Romania

The Roma people are the largest minority in Europe and, since centuries, have suffered discrimination and hate crimes which persist currently. This paper analyzes 4,136 comments (2016–2020) about the Roma posted on an online open-access forum. Our findings suggest that the factors influencing Romanians’ hostility against the Roma are: (1) the general distrust in the Romanian administrations, (2) the feeling of threat, and (3) the in-group favoritism. The article discusses strategies such as the improvement of the citizens’ trust in the public administration, pragmatic interventions bottom-up which aim to increase the social pacification, the redefinition of the political correctness, and the application of situational prevention techniques to prevent hate crimes

Facebook’s Dark Pattern Design, Public Relations and Internal Work Culture

Facebook inc. (now Meta Platforms) has been a target of several accusations regarding privacy issues, dark pattern design, spreading of disinformation and polarizing its users. Based on several leaked documents, the company’s public relations have often contradicted with its internal discussions and research. This study examines these issues by analyzing the leaked documents and published news articles. It outlines the dark patterns that the company has applied to their platform’s functionality, and discusses how they promote toxic behavior, hate speech and disinformation to flourish on the platform. The study also discusses some of the discrepancies between Facebook inc.’s public relations and internal work culture and discussions