Towards Automatic Detection of User Influence in Twitter by Means of Stylistic and Behavioral Features

Ramírez-de-la-Rosa, Gabriela; Villatoro-Tello, Esaú; Jiménez-Salazar, Héctor; Sánchez-Sánchez, Christian

doi:10.1007/978-3-319-13647-9_23

Cited by 18 publications

(28 citation statements)

References 10 publications

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“…The authors in [20] claim that a user's influential level can be detected by considering the writing style and behavior within the OSNs. Therefore, they proposed 23 features of user profile (e.g.…”

Section: User-oriented Matchingmentioning

confidence: 99%

Modeling Influence with Semantics in Social Networks

2020

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The discovery of influential entities in all kinds of networks (e.g. social, digital, or computer) has always been an important field of study. In recent years, Online Social Networks (OSNs) have been established as a basic means of communication and often influencers and opinion makers promote politics, events, brands or products through viral content. In this work, we present a systematic review across i) online social influence metrics, properties, and applications and ii) the role of semantic in modeling OSNs information. We found that both areas can jointly provide useful insights towards the qualitative assessment of viral user-generated content, as well as for modeling the dynamic properties of influential content and its flow dynamics.

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Section: User-oriented Matchingmentioning

confidence: 99%

Modeling Influence with Semantics in Social Networks

2020

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“…On the same note, the length of the username (Feature 9), expressed in characters, was used in some studies to identify certain types of users [46,62]. For instance, social capitalists tend to have very long names.…”

Section: User Profilementioning

confidence: 99%

“…Feature 28 is very similar, but for hapaxes, i.e. words which are unique to the user [62]. Put differently, this feature is about words only the considered user includes in his tweets.…”

Section: Lexical Aspectsmentioning

confidence: 99%

“…The classification performances are shown in terms of F -Score for each domain and averaged over domains, as explained in Section 3.2. For comparison purposes, we also reported in the same table the baseline, the results obtained by Ramírez-de-la-Rosa et al [62] using SVM, and those of Cossu et al [18], based on tweets content (Section 3.2).…”

Section: Classificationmentioning

confidence: 99%

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A review of features for the discrimination of twitter users: application to the prediction of offline influence

Cossu

Labatut

Dugué

2016

Soc. Netw. Anal. Min.

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Many works related to Twitter aim at characterizing its users in some way: role on the service (spammers, bots, organizations, etc.), nature of the user (socio-professional category, age, etc.), topics of interest, and others. However, for a given user classification problem, it is very difficult to select a set of appropriate features, because the many features described in the literature are very heterogeneous, with name overlaps and collisions, and numerous very close variants. In this article, we review a wide range of such features. In order to present a clear state-of-the-art description, we unify their names, definitions and relationships, and we propose a new, neutral, typology. We then illustrate the interest of our review by applying a selection of these features to the offline influence detection problem. This task consists in identifying users which are influential in real-life, based on their Twitter account and related data. We show that most features deemed efficient to predict online influence, such as the numbers of retweets and followers, are not relevant to this problem. However, we propose several content-based approaches to label Twitter users as Influencers or not. We also rank them according to a predicted influence level. Our proposals are evaluated over the CLEF RepLab 2014 dataset, and outmatch state-of-the-art methods.

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“…See Table IV for the numerical results. Moreover, UAMCLYR also considered NLP Quantitative Stylistic and Behavioral features extracted from tweet contents and extended their approach after the challenge [28].…”

Section: B Nlp Workmentioning

confidence: 99%

Detecting Real-World Influence through Twitter

Cossu

Dugué

Labatut

2015

2015 Second European Network Intelligence Conference

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Abstract-In this paper, we investigate the issue of detecting the real-life influence of people based on their Twitter account. We propose an overview of common Twitter features used to characterize such accounts and their activity, and show that these are inefficient in this context. In particular, retweets and followers numbers, and Klout score are not relevant to our analysis. We thus propose several Machine Learning approaches based on Natural Language Processing and Social Network Analysis to label Twitter users as Influencers or not. We also rank them according to a predicted influence level. Our proposals are evaluated over the CLEF RepLab 2014 dataset, and outmatch state-of-the-art ranking methods.

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Towards Automatic Detection of User Influence in Twitter by Means of Stylistic and Behavioral Features

Cited by 18 publications

References 10 publications

Modeling Influence with Semantics in Social Networks

Modeling Influence with Semantics in Social Networks

A review of features for the discrimination of twitter users: application to the prediction of offline influence

Detecting Real-World Influence through Twitter

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