Semantic-based Followee Recommendations on Twitter Network

Dib, Brahim; Kalloubi, Fahd; Nfaoui, El Habib; Boulaalam, Abdelhak

doi:10.1016/j.procs.2018.01.149

Cited by 8 publications

(2 citation statements)

References 3 publications

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“…We downloaded more than 1,000,000 tweets of 20,000 Twitter users using Twitter API, applying follower/followee topology (Dib et al , 2018; Armentano et al , 2013) as demonstrated below.…”

Section: Experiments Resultsmentioning

confidence: 99%

“…Different categories of recommendation systems were studied such as collaborative filtering systems, Kim and Shim (2011) proposed a user recommendation system for Twitter, called TWITOBI, that used a collaborative filtering model that recommends top- n users to follow and top- n tweets to read, in other paper, Kim and Shim (2014) presented a followee recommendation system using a probabilistic model based on LDA, it can detect the real process of posting tweets. In Dib et al (2018) a Twitter followee recommendation system that relied on semantic analysis of users’ profiles content was proposed. A similar solution was proposed in Dib et al (2020), it leverages the topic feature of tweets.…”

Section: Related Workmentioning

confidence: 99%

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Incorporating LDA with LSTM for followee recommendation on Twitter network

Dib

Kalloubi

Nfaoui

et al. 2021

IJWIS

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Purpose The purpose of this study is to facilitate the task of finding appropriate information to read about, and searching for people who are in the same field of interest. Knowing that more people keep up with new streaming information on Twitter micro-blogging service. With the immense number of micro-posts shared via the follower/followee network graph, Twitter users find themselves in front of millions of tweets, which makes the task crucial. Design/methodology/approach In this paper, a long short–term memory (LSTM) model that relies on the latent Dirichlet allocation (LDA) output vector for followee recommendation, the LDA model applied as a topic modeling strategy is proposed. Findings This study trains the model using a real-life data set extracted based on Twitter follower/followee architecture. It confirms the effectiveness and scalability of the proposed approach. The approach improves the state-of-the-art models average-LSTM and time-LSTM. Research limitations/implications This study improves mainly the existing followee recommendation systems. Because, unlike previous studies, it applied a non-hand-crafted method which is the LSTM neural network with LDA model for topics extraction. The main limitation of this study is the cold-start users cannot be treated, also some active fake accounts may not be detected. Practical implications The aim of this approach is to assist users seeking appropriate information to read about, by choosing appropriate profiles to follow. Social implications This approach consolidates the social relationship between users in a microblogging platform by suggesting like-minded people to each other. Thus, finding users with the same interests will be easy without spending a lot of time seeking relevant users. Originality/value Instead of classic recommendation models, the paper provides an efficient neural network searching method to make it easier to find appropriate users to follow. Therefore, affording an effective followee recommendation system.

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“…We downloaded more than 1,000,000 tweets of 20,000 Twitter users using Twitter API, applying follower/followee topology (Dib et al , 2018; Armentano et al , 2013) as demonstrated below.…”

Section: Experiments Resultsmentioning

confidence: 99%