# Cognitive network science for understanding online social cognitions: A brief review

arXiv: https://arxiv.org/abs/2102.12799 · HTML: https://mlsanity.com/paper/2102.12799/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 81 references that are in the map.
- First posted on arXiv 25 Feb 2021.

## Builds on (most cited first, 18 of 18)

- [The Rise of Social Bots](https://mlsanity.com/paper/1407.5225/) (2014)
- [The COVID-19 Social Media Infodemic](https://mlsanity.com/paper/2003.05004/) (2020)
- [The structure and dynamics of multilayer networks](https://mlsanity.com/paper/1407.0742/) (2014)
- [The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race](https://mlsanity.com/paper/1701.03017/) (2017)
- [Echo Chambers: Emotional Contagion and Group Polarization on Facebook](https://mlsanity.com/paper/1607.01032/) (2016)
- [Bots increase exposure to negative and inflammatory content in online social systems](https://mlsanity.com/paper/1802.07292/) (2018)
- [Divergent discourse between protests and counter-protests: #BlackLivesMatter and #AllLivesMatter](https://mlsanity.com/paper/1606.06820/) (2016)
- [Anti dependency distance minimization in short sequences. A graph theoretic approach](https://mlsanity.com/paper/1906.05765/) (2019)
- [Multiplex lexical networks reveal patterns in early word acquisition in children](https://mlsanity.com/paper/1609.03207/) (2016)
- [#lockdown: network-enhanced emotional profiling at the times of COVID-19](https://mlsanity.com/paper/2005.04404/) (2020)
- [Text-mining forma mentis networks reconstruct public perception of the STEM gender gap in social media](https://mlsanity.com/paper/2003.08835/) (2020)
- [Revealing semantic and emotional structure of suicide notes with cognitive network science](https://mlsanity.com/paper/2007.12053/) (2020)
- [Analysing Twitter Semantic Networks: the case of 2018 Italian Elections](https://mlsanity.com/paper/2009.02960/) (2020)
- [Word sense induction using word embeddings and community detection in complex networks](https://mlsanity.com/paper/1803.08476/) (2018)
- [GEFF: Graph Embedding for Functional Fingerprinting](https://mlsanity.com/paper/2001.06605/) (2020)
- [Analysis of English free association network reveals mechanisms of efficient solution of Remote Association Tests](https://mlsanity.com/paper/2008.12219/) (2020)
- [Structure-semantics interplay in complex networks and its effects on the predictability of similarity in texts](https://mlsanity.com/paper/1303.0350/) (2013)
- [From Topic Networks to Distributed Cognitive Maps: Zipfian Topic Universes in the Area of Volunteered Geographic Information](https://mlsanity.com/paper/2002.01454/) (2020)

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

- [DASentimental: Detecting depression, anxiety and stress in texts via emotional recall, cognitive networks and machine learning](https://mlsanity.com/paper/2110.13710/) (2021)
- [Writing about COVID-19 vaccines: Emotional profiling unravels how mainstream and alternative press framed AstraZeneca, Pfizer and vaccination campaigns](https://mlsanity.com/paper/2201.07538/) (2022)
- [Cognitive networks identify the content of English and Italian popular posts about COVID-19 vaccines: Anticipation, logistics, conspiracy and loss of trust](https://mlsanity.com/paper/2103.15909/) (2021)
- [Cognitive network science quantifies feelings expressed in suicide letters and Reddit mental health communities](https://mlsanity.com/paper/2110.15269/) (2021)
- [Cognitive networks highlight differences and similarities in the STEM mindsets of human and LLM-simulated trainees, experts and academics](https://mlsanity.com/paper/2502.19529/) (2025)

## Abstract (arXiv)

Social media are digitalising massive amounts of users' cognitions in terms of timelines and emotional content. Such Big Data opens unprecedented opportunities for investigating cognitive phenomena like perception, personality and information diffusion but requires suitable interpretable frameworks. Since social media data come from users' minds, worthy candidates for this challenge are cognitive networks, models of cognition giving structure to mental conceptual associations. This work outlines how cognitive network science can open new, quantitative ways for understanding cognition through online media, like: (i) reconstructing how users semantically and emotionally frame events with contextual knowledge unavailable to machine learning, (ii) investigating conceptual salience/prominence through knowledge structure in social discourse; (iii) studying users' personality traits like openness-to-experience, curiosity, and creativity through language in posts; (iv) bridging cognitive/emotional content and social dynamics via multilayer networks comparing the mindsets of influencers and followers. These advancements combine cognitive-, network- and computer science to understand cognitive mechanisms in both digital and real-world settings but come with limitations concerning representativeness, individual variability and data integration. Such aspects are discussed along the ethical implications of manipulating socio-cognitive data. In the future, reading cognitions through networks and social media can expose cognitive biases amplified by online platforms and relevantly inform policy making, education and markets about massive, complex cognitive trends.