# The Impact of Time on Hashtag Reuse in Twitter: A Cognitive-Inspired Hashtag Recommendation Approach

## TL;DR

This paper introduces a cognitive-inspired hashtag recommendation method based on the Base-Level Learning equation, modeling temporal reuse patterns of hashtags on Twitter, and demonstrates its superior performance over existing approaches.

## Contribution

The paper adapts the BLL equation from cognitive architecture to improve hashtag recommendation by capturing temporal reuse patterns in Twitter data.

## Key findings

- Outperforms state-of-the-art hashtag recommendation methods
- Effective in modeling individual and social hashtag reuse patterns
- Validated on two real Twitter datasets

## Abstract

In our work [KPL17], we study temporal usage patterns of Twitter hashtags, and we use the Base-Level Learning (BLL) equation from the cognitive architecture ACT-R [An04] to model how a person reuses her own, individual hashtags as well as hashtags from her social network. The BLL equation accounts for the time-dependent decay of item exposure in human memory. According to BLL, the usefulness of a piece of information (e.g., a hashtag) is defined by how frequently and how recently it was used in the past, following a time-dependent decay that is best modeled with a power-law distribution. We used the BLL equation in our previous work to recommend tags in social bookmarking systems [KL16]. Here [KPL17], we adopt the BLL equation to model temporal reuse patterns of individual (i.e., reusing own hashtags) and social hashtags (i.e., reusing hashtags, which has been previously used by a followee) and to build a cognitive-inspired hashtag recommendation algorithm. We demonstrate the efficacy of our approach in two empirical social networks crawled from Twitter, i.e., CompSci and Random (for details about the datasets, see [KPL17]). Our results show that our approach can outperform current state-of-the-art hashtag recommendation approaches.