Identifying topical authorities in microblogs

Pal, Aditya; Counts, Scott

doi:10.1145/1935826.1935843

Cited by 287 publications

(227 citation statements)

References 17 publications

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“…The idea of topic-sensitive PageRank was later used and adjusted for social networks such as Twitter for ranking topic-based user influence. Also, topical authorities studied by Pal and Counts (2011). They proposed a Gaussian-based ranking to rank users efficiently.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Topic-based Social Influence Measurement for Social Networks

Hamzehei

Jiang²,

Koutra

et al. 2017

AJIS

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Social science studies have acknowledged that the social influence of individuals is not identical. Social networks structure and shared text can reveal immense information about users, their interests, and topic-based influence. Although some studies have considered measuring user influence, less has been on measuring and estimating topic-based user influence. In this paper, we propose an approach that incorporates network structure, usergenerated content for topic-based influence measurement, and user's interactions in the network. We perform experimental analysis on Twitter data and show that our proposed approach can effectively measure topic-based user influence.

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Section: Related Workmentioning

confidence: 99%

“…Opinion leaders and discussion starters also have been studied as a measure of social influence (Jabeur, Tamine et al 2012). A user's position in the network (Jin and Wang 2013), content (Hu, Fang et al 2013), and activities (Pal and Counts 2011) have been also studied as influence measures.…”

Section: Introductionmentioning

confidence: 99%

Topic-based Social Influence Measurement for Social Networks

Hamzehei

Jiang²,

Koutra

et al. 2017

AJIS

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“…They used PageRank algorithm to identify authorities on each topic. Pal A. and Counts S. [19] also proposed to find the most interesting and authoritative authors for any given topic in Twitter. They analyzed features such as user's content and combined it their friends and followers information to be affective to find topical experts.…”

Section: Related Work For Using Expert Knowledgementioning

confidence: 99%

Measuring Opinion Credibility in Twitter

Thandar

Usanavasin

2015

Advances in Intelligent Systems and Computing

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sentiment analyzer and opinion credibility calculator. We use two machine learning techniques: Naïve Bayes (NB) and Support Vector Machine (SVM) for sentiment analyzer. To define expert knowledge, we use author's profile, list features and author's tweets behaviour for weight factors in twitter. In opinion calculator, we produce opinion credibility value combine with sentiment analyzer result and expert knowledge score.Thereafter we converse all of our defined author's expert weight factors to analyses which weight factors weightier than others and which factors can support more credible when we compute opinion credibility. To evaluate our method we used weighted kappa statistic for accuracy of opinion credibility.

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“…Bakshy et al [18] focus on Twitter influencers who are roots of large cascades and have many followers, while Pal et al [19] adopt clustering and ranking based on structural and content characteristics to discover authoritative users. Although the above works are similar to ours in that they focus on influence structures and user summaries, our genotype targets capturing the invariant user behavior and information spread within topics as a whole, involving a collection of topically related information parcels.…”

Section: Related Workmentioning

confidence: 99%

Modeling individual topic-specific behavior and influence backbone networks in social media

Bogdanov

Busch

Moehlis

et al. 2014

Soc. Netw. Anal. Min.

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Information propagation in social media depends not only on the static follower structure but also on the topic-specific user behavior. Hence novel models incorporating dynamic user behavior are needed. To this end, we propose a model for individual social media users, termed a genotype. The genotype is a per-topic summary of a user's interest, activity and susceptibility to adopt new information. We demonstrate that user genotypes remain invariant within a topic by adopting them for classification of new information spread in large-scale real networks. Furthermore, we extract topic-specific influence backbone structures based on content adoption and show that their structure differs significantly from the static follower network. We also find, at the population level using a simple contagion model, that hashtags of a known topic propagate at the greatest rate on backbone networks of the same topic. When employed for influence prediction of new content spread, our genotype model and influence backbones enable more than 20% improvement, compared to purely structural features. It is also demonstrated that knowledge of user genotypes and influence backbones allows for the design of effective strategies for latency minimization of topic-specific information spread.

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Identifying topical authorities in microblogs

Cited by 287 publications

References 17 publications

Topic-based Social Influence Measurement for Social Networks

Topic-based Social Influence Measurement for Social Networks

Measuring Opinion Credibility in Twitter

Modeling individual topic-specific behavior and influence backbone networks in social media

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