# The Role of Network and Identity in the Diffusion of Hashtags

arXiv: https://arxiv.org/abs/2407.12771 · HTML: https://mlsanity.com/paper/2407.12771/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 104 references that are in the map.
- First posted on arXiv 17 Jul 2024.

## Builds on (most cited first, 14 of 14)

- [DeepCas: an End-to-end Predictor of Information Cascades](https://mlsanity.com/paper/1611.05373/) (2016)
- [Can Cascades be Predicted?](https://mlsanity.com/paper/1403.4608/) (2014)
- [Demographic Inference and Representative Population Estimates from Multilingual Social Media Data](https://mlsanity.com/paper/1905.05961/) (2019)
- [Where New Words Are Born: Distributional Semantic Analysis of Neologisms and Their Semantic Neighborhoods](https://mlsanity.com/paper/2001.07740/) (2020)
- [Making "fetch" happen: The influence of social and linguistic context on nonstandard word growth and decline](https://mlsanity.com/paper/1802.04140/) (2018)
- [Geotagging One Hundred Million Twitter Accounts with Total Variation Minimization](https://mlsanity.com/paper/1404.7152/) (2014)
- [The Social Dynamics of Language Change in Online Networks](https://mlsanity.com/paper/1609.02075/) (2016)
- [Socioeconomic Dependencies of Linguistic Patterns in Twitter: A Multivariate Analysis](https://mlsanity.com/paper/1804.01155/) (2018)
- [Demographic Representation and Collective Storytelling in the Me Too Twitter Hashtag Activism Movement](https://mlsanity.com/paper/2010.06472/) (2020)
- [Do Cascades Recur?](https://mlsanity.com/paper/1602.01107/) (2016)
- [Communicative need modulates competition in language change](https://mlsanity.com/paper/2006.09277/) (2020)
- [Capturing Dynamics of Information Diffusion in SNS: A Survey of Methodology and Techniques](https://mlsanity.com/paper/2110.14245/) (2021)
- [Understanding and forecasting lifecycle events in information cascades](https://mlsanity.com/paper/1809.06050/) (2018)
- [#Bigbirds Never Die: Understanding Social Dynamics of Emergent Hashtag](https://mlsanity.com/paper/1303.7144/) (2013)

## Built on by (most cited first, 1 of 1)

- [A Pressure-Based Diffusion Model for Influence Maximization on Social Networks](https://mlsanity.com/paper/2509.12822/) (2025)

## Abstract (arXiv)

The diffusion of culture online is theorized to be influenced by many interacting social factors (e.g., network and identity). However, most existing computational cascade models consider just a single factor (e.g., network or identity). This work offers a new framework for teasing apart the mechanisms underlying hashtag cascades. We curate a new dataset of 1,337 hashtags representing cultural innovation online, develop a 10-factor evaluation framework for comparing empirical and simulated cascades, and show that a combined network+identity model better simulates hashtag cascades than network- or identity-only counterfactuals. We also explore heterogeneity in performance: While a combined network+identity model best predicts the popularity of cascades, a network-only model best predicts cascade growth and an identity-only model best predicts adopter composition. The network+identity model has the highest comparative advantage among hashtags used for expressing racial or regional identity and talking about sports or news. In fact, we are able to predict what combination of network and/or identity best models each hashtag and use this to further improve performance. Our results show the utility of models incorporating the interactions of network, identity, and other social factors in the diffusion of hashtags in social media.