Prominent Users Detection during Specific Events by Learning On- and Off-topic Features of User Activities

Bizid, Imen; Nayef, Nibal; Boursier, Patrice; Faïz, Sami; Morcos, Jacques J.

doi:10.1145/2808797.2809411

Cited by 15 publications

(12 citation statements)

References 16 publications

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“…Twitter is one of the popular social media platforms that provide the facility of online microblogging services. Some of the works identify hidden communities and relationships between different users (Java et al, 2007;Kuncheva & Montana, 2015;Bizid et al, 2015). Twitter is a widely accessible platform and used for opinion mining from available tweets (O'Connor et al, 2010).…”

Section: Related Workmentioning

confidence: 99%

Multi-class approach for user behavior prediction using deep learning framework on twitter election dataset

Mohbey

2019

J. of Data, Inf. and Manag.

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Among the broad assortment of Machine Learning approaches, deep learning has recently attracted attention particularly in the domain of user behavior analysis. The notion to study user behavior from the unstructured tweets shared on social media is an interesting yet challenging task. A social platform such as Twitter yield access to the unprompted views of the wide-ranging users on particular events like election. These views cater government and corporates to remold strategies, assess the areas where better measures need to be put forward and monitor common opinion. With the advent of the general election in India (largest democracy) people tend to articulate their views or issues. Tweets related to general elections 2019 of India is used as data corpus for the study. Multi-class classification fabricated with novel deep learning approach is implemented to analyses the user opinion. Here, we have used nine different classes, which is representing larger issues in the nation for election agenda. Moreover, comparative analysis between tradition approaches such as Naïve Bayes, SVM, decision tree, logistic regression and employed approach with deep learning method is presented. Experimental results revels that the proposed model can reach up to 98.70% accuracy on multiclass based prediction in machine learning. The results assist the government and businesses to know about grave issue offering a shot to revise strategic policy and make welfare scheme program.

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Section: Related Workmentioning

confidence: 99%

Multi-class approach for user behavior prediction using deep learning framework on twitter election dataset

Mohbey

2019

J. of Data, Inf. and Manag.

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“…Finally, [4] presents a model for identifying "prominent users" regarding a specific topic event in Twitter. Those are users who focus their attention and communication on the aforementioned topic event.…”

Section: Related Workmentioning

confidence: 99%

A Customisable Pipeline for Continuously Harvesting Socially-Minded Twitter Users

Primo

Missier

Romanovsky

et al. 2019

Lecture Notes in Computer Science

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On social media platforms and Twitter in particular, specific classes of users such as influencers have been given satisfactory operational definitions in terms of network and content metrics. Others, for instance online activists, are not less important but their characterisation still requires experimenting. We make the hypothesis that such interesting users can be found within temporally and spatially localised contexts, i.e., small but topical fragments of the network containing interactions about social events or campaigns with a significant footprint on Twitter. To explore this hypothesis, we have designed a continuous user profile discovery pipeline that produces an ever-growing dataset of user profiles by harvesting and analysing contexts from the Twitter stream. The profiles dataset includes key network and content-based users metrics, enabling experimentation with user-defined score functions that characterise specific classes of online users. The paper describes the design and implementation of the pipeline and its empirical evaluation on a case study consisting of healthcare-related campaigns in the UK, showing how it supports the operational definitions of online activism, by comparing three experimental ranking functions. The code is publicly available.

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“…Imen [9] et al present a supervised learning classification model to identify the prominent users under the specific topic. Manuel [10] proposes NETINF algorithm which defines cascade propagation model to infer implicit networks of influence and diffusion. Tang [4] analyzed topic-level social influence by proposing TAP method to apply large scale networks.…”

Section: Related Workmentioning

confidence: 99%

Using Multi-level Features Construction for Discovering Key Twitterers

Wu¹

2017

Proceedings of the Advances in Materials, Machinery, Electrical Engineering (AMMEE 2017)

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Abstract. Identifying key users have become a focal problem in the area of online social networks. Tweets and Behaviors of people are two core facets for finding key users. Tweets of published by users spread support from different behaviors. However, existing literature on key users evaluation has mainly focused on methods based on one of them effects in social network, which make topic and behavior latent dimensions to difficult to interpret. How to modeling roles of user revealing the hidden relation based on tweets that users interested in and behaviors for dissemination of information in real social networks? In this paper, we tackle this problem by focusing on different behaviors of users and tweets similarity based on word embedding to measure the influence of users in social networks. We propose an algorithm using relation feature construction for key twitters extraction. Through extensive experiments comparing with different algorithms, we demonstrate that model is able to identify key users. Additional, the model that can be used to facilitate other tasks such as automated latent community discovery, and to track origin users.

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Prominent Users Detection during Specific Events by Learning On- and Off-topic Features of User Activities

Cited by 15 publications

References 16 publications

Multi-class approach for user behavior prediction using deep learning framework on twitter election dataset

Multi-class approach for user behavior prediction using deep learning framework on twitter election dataset

A Customisable Pipeline for Continuously Harvesting Socially-Minded Twitter Users

Using Multi-level Features Construction for Discovering Key Twitterers

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