The Influence in Twitter: Are They Really Influenced?

Sung, Juyup; Moon, Seunghyeon; Lee, Jae-Gil

doi:10.1007/978-3-319-04048-6_9

Cited by 12 publications

(6 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

366

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

366

230

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“…Similarly, Hu et al (2013) worked on detecting topical authorities with the assumption that retweeting propagates topical authority. 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.…”

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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“…Here, we can find two approaches: Based only on the central position of the user (topology-based approaches: Degree centrality, closeness centrality, and betweenness centrality [142]), or more complex, which takes into account more aspects, like a user's history, content, and other influence properties. Many methods which used topology criteria are based on PageRank [23,56,128,138].…”

Section: Influential Users' Discoverymentioning

confidence: 99%

Forecasting Cryptocurrency Value by Sentiment Analysis: An HPC-Oriented Survey of the State-of-the-Art in the Cloud Era

Zamuda

Crescimanna

Burguillo

et al. 2019

Lecture Notes in Computer Science

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This chapter surveys the state-of-the-art in forecasting cryptocurrency value by Sentiment Analysis. Key compounding perspectives of current challenges are addressed, including blockchains, data collection, annotation, and filtering, and sentiment analysis metrics using data streams and cloud platforms. We have explored the domain based on this problem-solving metric perspective, i.e., as technical analysis, forecasting, and estimation using a standardized ledger-based technology. The envisioned tools based on forecasting are then suggested, i.e., ranking

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The Influence in Twitter: Are They Really Influenced?

Cited by 12 publications

References 14 publications

Measuring user influence on Twitter: A survey

Measuring user influence on Twitter: A survey

Collaborative topic regression for predicting topic-based social influence

Forecasting Cryptocurrency Value by Sentiment Analysis: An HPC-Oriented Survey of the State-of-the-Art in the Cloud Era

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