Detection of Rapidly Spreading Hashtags via Social Networks

Abstract

Social network services (SNSs) such as Twitter and Facebook have emerged as a new medium for communication. They offer a unique mechanism of sharing information by allowing users to receive all messages posted by those whom they ''follow''. As information in today's SNSs often spreads in the form of hashtags, detecting rapidly spreading hashtags in SNSs has recently attracted much attention. In this paper, we propose realistic epidemic models to describe the probabilistic process of hashtag propagation. Our models take into account the way how users communicate in SNSs; moreover the models consider the influence of external media and separate it from internal diffusion within networks. Based on the proposed models, we develop efficient inference algorithms that measure the propagation rates of hashtags in social networks. With real-life social network data including hashtags and synthetic data obtained by simulating information diffusion, we show that the proposed algorithms find fast-spreading hashtags more accurately than existing algorithms. Moreover, our in-depth case study demonstrates that our algorithms correctly find internal diffusion rates of hashtags as well as external media influences.

Information Diffusion Model in Twitter: A Systematic Literature Review

“…This section intends to answer RQ2: What methods have researchers used regarding information diffusion models on Twitter? Based on our review of the information diffusion model on Twitter, various methods have been used by researchers, such as epidemic models [14,[42][43][44], the stochastic model [11,45,46], machine learning [47][48][49], and the independent cascade model (ICM) [50].…”

Hashtags Influence Maximization: Choosing the most Influential Hashtags on Instagram

“…Previous studies on identifying influential hashtags used the PageRank algorithm [14] and independent cascade diffusion model [7] to obtain information from the hashtags graph. However, both studies used hashtags usage count as the ground truth.…”

“…Identification of influential hashtags has been previously studied [7]; however, it used hashtag's virality (usage count) as the main metric. As previously stated, a high usage count doesn't directly correlate to likes.…”

Evaluating the use of Instagram images color histograms and hashtags sets for automatic image annotation

“…This is because hashtags have some unique characteristics. They represent a specific topic, or idea, or annotation of an image or text, and they are used regardless of the topic they annotate (Kim and Seo, 2020 ). In addition, according to Gomez et al ( 2020 ), hashtags is a form of image tagging.…”