Tell me something my friends do not know: diversity maximization in social networks

Abstract

Social media have a great potential to improve information dissemination in our society, yet they have been held accountable for a number of undesirable effects, such as polarization and filter bubbles. It is thus important to understand these negative phenomena and develop methods to combat them. In this paper, we propose a novel approach to address the problem of breaking filter bubbles in social media. We do so by aiming to maximize the diversity of the information exposed to connected social-media users. We formulate the problem of maximizing the diversity of exposure as a quadratic-knapsack problem. We show that the proposed diversity-maximization problem is inapproximable, and thus, we resort to polynomial nonapproximable algorithms, inspired by solutions developed for the quadratic-knapsack problem, as well as scalable greedy heuristics. We complement our algorithms with instance-specific upper bounds, which are used to provide empirical approximation guarantees for the given problem instances. Our experimental evaluation shows that a proposed greedy algorithm followed by randomized local search is the algorithm of choice given its quality-vs.-efficiency trade-off.

Analyzing Barriers to Digital Transformation in the German Engineering Industry – A Comparison of Digitalized and Non-Digitalized Enterprises

“…From a sampling perspective, the assignment of the respondents via social media could be problematic, as the sample might demonstrate bias and even underestimate the barriers. Social networks might provoke a filter bubble [51] and not every employee might be active on these networks. Since our study aimed to measure perceptions about DT and associated barriers, recruitment in professional digital networks was considered a valid sampling strategy.…”

La crisis de la Covid-19 como oportunidad política. El uso de Twitter por Santiago Abascal durante el estado de alarma en España (14 de marzo-20 de junio de 2020)

“…Pariser (2011) en los primeros años del desarrollo de las redes sociales se refiere a los "filtros burbuja", que propician un acceso a la información acorde a los intereses de cada usuario. Estos fenómenos han sido objeto de estudios a través del análisis del comportamiento de los algoritmos, que proceden al filtrado de la información accesible a través de las redes sociales (Berman;Katona, 2019) o que proponen alternativas para superar la atomización de contenidos y mensajes (Matakos;Tu;Gionis, 2020).…”

Causality-based Social Media Analysis for Normal Users Credibility Assessment in a Political Crisis

“…In general, many social network community detection algorithms have been adopted [27]. Previous studies tried to tackle such problem by assuming that if we enforced more information diversity to each social bubble, it would reduce the polarization since the latter is an effect of the lack of information diversity itself [28]. Other previous work aimed to detect these communities and identify them as sub-networks or similar connected nodes in the social graph by analyzing the network cohesion [29].…”

“…Some literature considered the less diversity of social content as a major cause of polarization, that means the more diversity of content a user is exposed to, the less polarized the user could be in most cases [28]. Furthermore, polarization is not only influencing normal users trustworthiness, it also dictates the objectively active deceptive accounts on the network.…”

“…4. Polarized political social media discussion causal graph exposure, the latter is directly influencing both polarization levels and social engagements as well [28]. Eventually, we consider other social engagement that might influence one's engagement like when a user is replying to others and approving or denying their opinions.…”