# Content and Network Dynamics Behind Egyptian Political Polarization on Twitter

arXiv: https://arxiv.org/abs/1410.3097 · HTML: https://mlsanity.com/paper/1410.3097/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Oct 2014.

## Built on by (most cited first, 4 of 4)

- [Improving (Dis)agreement Detection with Inductive Social Relation Information From Comment-Reply Interactions](https://mlsanity.com/paper/2302.03950/) (2023)
- [DoubleH: Twitter User Stance Detection via Bipartite Graph Neural Networks](https://mlsanity.com/paper/2301.08774/) (2023)
- [Detecting Propaganda on the Sentence Level during the COVID-19 Pandemic](https://mlsanity.com/paper/2108.12269/) (2021)
- [Automatic Expansion and Retargeting of Arabic Offensive Language Training](https://mlsanity.com/paper/2111.09574/) (2021)

## Abstract (arXiv)

There is little doubt about whether social networks play a role in modern protests. This agreement has triggered an entire research avenue, in which social structure and content analysis have been central --but are typically exploited separately. Here, we combine these two approaches to shed light on the opinion evolution dynamics in Egypt during the summer of 2013 along two axes (Islamist/Secularist, pro/anti-military intervention). We intend to find traces of opinion changes in Egypt's population, paralleling those in the international community --which oscillated from sympathetic to condemnatory as civil clashes grew. We find little evidence of people "switching" sides, along with clear changes in volume in both pro- and anti-military camps. Our work contributes new insights into the dynamics of large protest movements, specially in the aftermath of the main events --rather unattended previously. It questions the standard narrative concerning a simplistic mapping between Secularist/pro-military and Islamist/anti-military. Finally, our conclusions provide empirical validation to sociological models regarding the behavior of individuals in conflictive contexts.