# Temporal Effects on Hashtag Reuse in Twitter: A Cognitive-Inspired Hashtag Recommendation Approach

arXiv: https://arxiv.org/abs/1701.01276 · HTML: https://mlsanity.com/paper/1701.01276/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Jan 2017.

## Built on by (most cited first, 6 of 6)

- [NASRec: Weight Sharing Neural Architecture Search for Recommender Systems](https://mlsanity.com/paper/2207.07187/) (2022)
- [Semantic Journeys: Quantifying Change in Emoji Meaning from 2012-2018](https://mlsanity.com/paper/2105.00846/) (2021)
- [Transformers Meet ACT-R: Repeat-Aware and Sequential Listening Session Recommendation](https://mlsanity.com/paper/2408.16578/) (2024)
- [Non-Parametric Temporal Adaptation for Social Media Topic Classification](https://mlsanity.com/paper/2209.05706/) (2022)
- [A conceptual model for leaving the data-centric approach in machine learning](https://mlsanity.com/paper/2302.03361/) (2023)
- [#REVAL: a semantic evaluation framework for hashtag recommendation](https://mlsanity.com/paper/2305.18330/) (2023)

## Abstract (arXiv)

Hashtags have become a powerful tool in social platforms such as Twitter to categorize and search for content, and to spread short messages across members of the social network. In this paper, we study temporal hashtag usage practices in Twitter with the aim of designing a cognitive-inspired hashtag recommendation algorithm we call BLLi,s. Our main idea is to incorporate the effect of time on (i) individual hashtag reuse (i.e., reusing own hashtags), and (ii) social hashtag reuse (i.e., reusing hashtags, which has been previously used by a followee) into a predictive model. For this, we turn to the Base-Level Learning (BLL) equation from the cognitive architecture ACT-R, which accounts for the time-dependent decay of item exposure in human memory. We validate BLLi,s using two crawled Twitter datasets in two evaluation scenarios: firstly, only temporal usage patterns of past hashtag assignments are utilized and secondly, these patterns are combined with a content-based analysis of the current tweet. In both scenarios, we find not only that temporal effects play an important role for both individual and social hashtag reuse but also that BLLi,s provides significantly better prediction accuracy and ranking results than current state-of-the-art hashtag recommendation methods.