# Viewpoint Discovery and Understanding in Social Networks

arXiv: https://arxiv.org/abs/1810.11047 · HTML: https://mlsanity.com/paper/1810.11047/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Oct 2018.

## Built on by (most cited first, 5 of 5)

- [It's the Same Old Story! Enriching Event-Centric Knowledge Graphs by Narrative Aspects](https://mlsanity.com/paper/2205.03876/) (2022)
- [Migration Reframed? A multilingual analysis on the stance shift in Europe during the Ukrainian crisis](https://mlsanity.com/paper/2302.02813/) (2023)
- [Lost in Recursion: Mining Rich Event Semantics in Knowledge Graphs](https://mlsanity.com/paper/2404.16405/) (2024)
- [A Conceptual Model for Attributions in Event-Centric Knowledge Graphs](https://mlsanity.com/paper/2503.03563/) (2025)
- [A Community-Based Approach for Stance Distribution and Argument Organization](https://mlsanity.com/paper/2604.16852/) (2026)

## Abstract (arXiv)

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.