Social influence analysis in microblogging platforms – A topic-sensitive based approach

Cano, Amparo Elizabeth; Mazumdar, Suvodeep; Ciravegna, Fabio

doi:10.3233/sw-130108

Cited by 18 publications

(10 citation statements)

References 14 publications

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“…Alpha centrality [97] Ω(T + n 2.3727 ) T -index [67] Ω(T + n) Information Diffusion [99] limited request Topic-Specific Author [70] limited request By effective audience [127] limited request TRank [90] limited request RetweetRank [147] Ω(T + n 2.3727 ) MentionRank [147] Ω(T + n 2.3727 ) TwitterRank [144] limited request InterRank [129] limited request Topic-Entity PageRank [16] Ω(T + n 2.3727 ) TIURank [83] limited request ARI [54] Ω(T + n 2.3727 ) Twitter user rank [95] Ω(T + n 2.3727 ) TS-SRW [63] Ω(T + n 2.3727 ) Topical Authority [57] Ω(T + n 2.3727 ) IARank [17] Ω(T · k 2 ) SNI [53] Ω(T + n 2.3727 ) By polarity & others [8] limited request By sucesptibility... [76] limited request By tweets graph [126] ? (high) WRA [152] limited request FLDA [5] ?…”

Section: Topical-sensitivementioning

confidence: 99%

Measuring user influence on Twitter: A survey

Riquelme

González-Cantergiani

2016

Information Processing & Management

368

230

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Abstract. Centrality is one of the most studied concepts in social network analysis. There is a huge literature regarding centrality measures, as ways to identify the most relevant users in a social network. The challenge is to find measures that can be computed efficiently, and that can be able to classify the users according to relevance criteria as close as possible to reality. We address this problem in the context of the Twitter network, an online social networking service with millions of users and an impressive flow of messages that are published and spread daily by interactions between users. Twitter has different types of users, but the greatest utility lies in finding the most influential ones. The purpose of this article is to collect and classify the different Twitter influence measures that exist so far in literature. These measures are very diverse. Some are based on simple metrics provided by the Twitter API, while others are based on complex mathematical models. Several measures are based on the PageRank algorithm, traditionally used to rank the websites on the Internet. Some others consider the timeline of publication, others the content of the messages, some are focused on specific topics, and others try to make predictions. We consider all these aspects, and some additional ones. Furthermore, we include measures of activity and popularity, the traditional mechanisms to correlate measures, and some important aspects of computational complexity for this particular context.

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Section: Topical-sensitivementioning

confidence: 99%

Measuring user influence on Twitter: A survey

Riquelme

González-Cantergiani

2016

Information Processing & Management

368

230

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“…Based on this, they design an influence model according to status information, users, and the relationship between them [16]. Basave et al find that adding information dimension to influence calculation through text mining and other information analysis methods can effectively improve the accuracy of the model [17].…”

Section: Influence Theorymentioning

confidence: 99%

Research on User Influence Model Integrating Personality Traits under Strong Connection

et al. 2020

Sustainability

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User influence has always been a major topic in the field of social networking. At present, most of the research focuses on three aspects: topological structure, social-behavioral dimension, and topic dimension and most of them ignore the difference between the audience. These models do not consider the impact of personality differences on user influences. To meet this need, this paper introduces the personality traits factor and proposes a user influence model which integrates personality traits (IPUIM) under a strong connection. The user influence measurement is constructed through the information dimension, structural dimension, and user behavioral dimension. The personality report of the user group is obtained by means of NEO-PI-R (The big five personality inventory, Chinese edition) and machine learning method, and it is integrated into the user influence model. The experiment proves that the model proposed in this paper has good accuracy and applicability in measuring user influence, and can effectively identify the key opinion leaders of different personality trait clusters.

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“…Sung et al (2013) proposed another extension of PageRank, and unlike Weng et al (2010), it does not need predefined topics for topic-based user influence. Cano et al (2014) introduced a PageRank-based user influence rank algorithm that the user links have weights based on their topics of interest similarities. The topic-based influence framework of Liu et al (2014), considers retweet frequency and link strength.…”

Section: Topic-based Influencementioning

confidence: 99%

Collaborative topic regression for predicting topic-based social influence

et al. 2019

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The rapid growth of social networks and their strong presence in our lives have attracted many researchers in social networks analysis. Users of social networks spread their opinions, get involved in discussions, and consequently, influence each other. However, the level of influence of different users is not the same. It varies not only among users, but also for one user across different topics. The structure of social networks and user-generated content can reveal immense information about users and their topic-based influence. Although many studies have considered measuring global user influence, measuring and estimating topic-based user influence has been under-explored. In this paper, we propose a collaborative topic-based social influence model that incorporates both network structure and user-generated content for topic-based influence measurement and prediction. We predict topic-based user influence on unobserved topics, based on observed topic-based user influence through their generated contents and activities in social networks. We perform experimental analysis on Twitter data, and show that our model outperforms state-of-the-art approaches on recall, accuracy, precision, and F-score for predicting topic-based user influence.

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Social influence analysis in microblogging platforms – A topic-sensitive based approach

Cited by 18 publications

References 14 publications

Measuring user influence on Twitter: A survey

Measuring user influence on Twitter: A survey

Research on User Influence Model Integrating Personality Traits under Strong Connection

Collaborative topic regression for predicting topic-based social influence

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