Scalable topic-specific influence analysis on microblogs

Bi, Bin; Tian, Yuanyuan; Sismanis, Yannis; Balmin, Andrey; Cho, Junghoo

doi:10.1145/2556195.2556229

Cited by 60 publications

(58 citation statements)

References 24 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] ? (high) Leadership [2] ?…”

Section: Topical-sensitivementioning

confidence: 99%

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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%

“…API REST allows to read or write data on Twitter through simple HTTP primitives like GET and POST. 5 For instance, if a GET is performed to the "https://api.twitter.com/1.1/trends/place.json?id=1" resource, then the API will return 50 global trending topics. We show in Listing 1.1 an extract of the answer to the previous query.…”

Section: Api Restmentioning

confidence: 99%

Measuring user influence on Twitter: A survey

Riquelme

González-Cantergiani

2016

Information Processing & Management

368

230

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“…These methods mainly use the link structures among the users to indicate users' social influence, ignoring the rich features provided by micro-blogging services. To address these limitations of previous approaches, a model named Followship-LDA is proposed in [5]. This model integrates both content topic discovery and social influence analysis in the same generative process.…”

Section: Related Workmentioning

confidence: 99%

Predicating Influential Microblog in Micro-blogging Services

Gao¹,

Wang²,

Li³

et al. 2017

dtcse

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Abstract. This paper focuses on the problem of predicating influential message (microblog) of micro-blogging services in China. Sina Weibo is one of China's most notable online social networks. Inferring influential microblog has been playing a crucial part in many applications such as branding management and online advertising. However, many previous social network analysis schemes rely mainly on link structures between users to find influencers in micro-blogging services, ignoring the important text content created by the users with the assuming that text content is the cause of links. As the text content has rich features such as word count of a single microblog, the sentiment of the microblog text and so on, we propose a novel model based on Learning to Rank, which integrates both features of the text content and the links of the Sina Weibo users to predicate the influential microblog. Experimental results show that our model outperforms many other related algorithms, including TunkRank and TwitterRank.

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“…Ghosh et al in [7] try to infer topical experts by leveraging crowd-sourced information from Twitter Lists. A very recent study [2] integrates topic modeling and influence analysis in the same model. Their proposed method called FLDA, it is a more complex mixture model than LDA which integrates probability distributions over topics with influence scores.…”

Section: Related Workmentioning

confidence: 99%

Determining Influential Users with Supervised Random Walks

Katsimpras

Vogiatzis

Παλιούρας

2015

Proceedings of the 24th International Conference on World Wide Web

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The emergence of social media and the enormous growth of social networks have initiated a great amount of research in social influence analysis. In this regard, many approaches take into account only structural information while a few have also incorporated content. In this study we propose a new method to rank users according to their topic-sensitive influence which utilizes a priori information by employing supervised random walks. We explore the use of supervision in a PageRank-like random walk while also exploiting textual information from the available content. We perform a set of experiments on Twitter datasets and evaluate our findings.

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Scalable topic-specific influence analysis on microblogs

Cited by 60 publications

References 24 publications

Measuring user influence on Twitter: A survey

Measuring user influence on Twitter: A survey

Predicating Influential Microblog in Micro-blogging Services

Determining Influential Users with Supervised Random Walks

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