Viewpoint Discovery and Understanding in Social Networks

Abstract

The Web has evolved to a dominant platform where everyone has the opportunity to express their opinions, to interact with other users, and to debate on emerging events happening around the world. On the one hand, this has enabled the presence of different viewpoints and opinions about a − usually controversial − topic (like Brexit), but at the same time, it has led to phenomena like media bias, echo chambers and filter bubbles, where users are exposed to only one point of view on the same topic. Therefore, there is the need for methods that are able to detect and explain the different viewpoints. In this paper, we propose a graph partitioning method that exploits social interactions to enable the discovery of different communities (representing different viewpoints) discussing about a controversial topic in a social network like Twitter. To explain the discovered viewpoints, we describe a method, called Iterative Rank Difference (IRD), which allows detecting descriptive terms that characterize the different viewpoints as well as understanding how a specific term is related to a viewpoint (by detecting other related descriptive terms). The results of an experimental evaluation showed that our approach outperforms state-of-the-art methods on viewpoint discovery, while a qualitative analysis of the proposed IRD method on three different controversial topics showed that IRD provides comprehensive and deep representations of the different viewpoints.

Beyond facts – a survey and conceptualisation of claims in online discourse analysis

“…As Thonet et al [177] explain, a viewpoint in a debate about the building of Israeli communities on disputed lands can for example be summarized as "pro-Palestine" or "pro-Israel". Consequently, Viewpoint Discovery is considered a sub-task of Opinion Mining [145,177].…”

“…in Quraishi et al [145], also providing a method to explain the discovered viewpoints by detecting descriptive terms that characterize them. Our model suggests, in line with the current research, that viewpoints with respect to topics take the form of polarized opinions.…”

It's the Same Old Story! Enriching Event-Centric Knowledge Graphs by Narrative Aspects

“…Viewpoint Detection. Research regarding the detection of viewpoints [23,36] and stances (see [1] for a recent survey in this field) have become increasingly popular in the last years. While those works mostly focus on the detection of viewpoints (clusters of similar opinions) and stances on certain topics in texts our work utilizes viewpoints only to define which documents can be used as witnesses for subjective attributions.…”

A Conceptual Tool to Eliminate Filter Bubbles in Social Networks

“…Another negative consequence that has been cited more specifically in the literature is the polarisation of political discussions in social media when people are trapped in a bubble that prevents them from receiving outsider information (Bakshy et al, 2015;Foth et al, 2016;Lahoti, Garimella, & Gionis, 2018;Quraishi, Fafalios, & Herder, 2018;Thonet et al, 2017;Yang et al, 2017). Previous literature has not found a significant relationship between exposure to an opposing political view and a change in people's political opinion (Bail et al, 2018).…”

“…However, recent studies have shifted the focus from content to the agent. Quraishi et al (2018) proposed a graph-partitioning method that exploits social interactions to represent different viewpoints in a social network. A qualitative evaluation of the proposed method is also presented based on implementing it on a dataset retrieved from Twitter.…”