Topical Influential User Analysis with Relationship Strength Estimation in Twitter

Liu, Xinyue; Shen, Hua; Ma, Fenglong; Liang, Wenxin

doi:10.1109/icdmw.2014.11

Cited by 18 publications

(15 citation statements)

References 33 publications

(52 reference statements)

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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

367

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

367

230

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“…These methods propose various ways of integrating this information into previous methods in order to provide a better evaluation of users' influence. [12][13][14] For instance, Xiao et al 15 have used hashtags in Twitter to identify influential users in the news-related communities and reported promising results.…”

Section: Literature Reviewmentioning

confidence: 99%

Influential post identification on Instagram through caption and hashtag analysis

Bashari

Fazl-Ersi

2020

Measurement and Control

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Influencer marketing through social networks is becoming an important alternative to traditional ways of advertising. Various solutions have been proposed that often take advantage of graph-based approaches to discover influencers in social networks. This paper designs a new method for the discovery of influential users in Instagram, by focusing on user-generated posts as an alternative source of information, to potentially augment the existing solutions based on network topology or connections. The text associated with each Instagram post potentially consists of a set of hashtags and a descriptive caption. Various word embedding methods such as Co-occurrence and fastText are examined to represent captions and hashtags. These representations are combined within a support vector machines framework to distinguish influential posts from non-influential ones. Extensive experiments show that the text data can play a significant role in identifying influential posts, and further demonstrate the strength of the proposed method for discovering influencers on Instagram.

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“…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. The link strength is estimated by poisson regression-based latent variable model on user's frequency of retweeting each other.…”

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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Topical Influential User Analysis with Relationship Strength Estimation in Twitter

Cited by 18 publications

References 33 publications

Measuring user influence on Twitter: A survey

Measuring user influence on Twitter: A survey

Influential post identification on Instagram through caption and hashtag analysis

Collaborative topic regression for predicting topic-based social influence

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