TwitterRank

Weng, Jianshu; Lim, Ee-Peng; Jiang, Jing; He, Qi

doi:10.1145/1718487.1718520

Cited by 1,414 publications

(141 citation statements)

References 13 publications

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“…TwitterRank [52] is an extension to the PageRank method which, in addition to link structure, takes into account the topical similarity between users in order to compute the influence one users wield onto the others. In that sense, TwitterRank is a topic-sensitive method which ranks users separately for different topics.…”

Section: Twitterrankmentioning

confidence: 99%

Nepotistic relationships in Twitter and their impact on rank prestige algorithms

Gayo-Avello

2013

Information Processing & Management

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Abstract. Micro-blogging services such as Twitter allow anyone to publish anything, anytime. Needless to say, many of the available contents can be diminished as babble or spam. However, given the number and diversity of users, some valuable pieces of information should arise from the stream of tweets. Thus, such services can develop into valuable sources of up-to-date information (the so-called real-time web) provided a way to find the most relevant/trustworthy/authoritative users is available. Hence, this makes a highly pertinent question for which graph centrality methods can provide an answer. In this paper the author offers a comprehensive survey of feasible algorithms for ranking users in social networks, he examines their vulnerabilities to linking malpractice in such networks, and suggests an objective criterion against which to compare such algorithms. Additionally, he suggests a first step towards -desensitizing‖ prestige algorithms against cheating by spammers and other abusive users.

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Section: Twitterrankmentioning

confidence: 99%

Nepotistic relationships in Twitter and their impact on rank prestige algorithms

Gayo-Avello

2013

Information Processing & Management

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“…[10,20]). Another interesting example related to PageRank and social networks is TwitterRank [49], an extension of PageRank that measures the relative influence of Twitter users in a certain topic. Like our own PageRank extension, TwitterRank is topic-specific: the random surfer jumps from one user to an acquaintance following topic-dependent probabilities.…”

Section: Related Workmentioning

confidence: 99%

“…Besides information retrieval, spectral ranking in general, and PageRank in particular, have been applied in social network analysis [4,49,38], scientometrics [40,34,15,51], geographic networks [16], and many other areas with great success.…”

Section: Pagerank In Contextmentioning

confidence: 99%

Endorsement deduction and ranking in social networks

Pérez-Rosés

Sebé

Ribó

2016

Computer Communications

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Some social networks, such as LinkedIn and ResearchGate, allow user endorsements for specific skills. In this way, for each skill we get a directed graph where the nodes correspond to users' profiles and the arcs represent endorsement relations. From the number and quality of the endorsements received, an authority score can be assigned to each profile. In this paper we propose an authority score computation method that takes into account the relations existing among different skills. Our method is based on enriching the information contained in the digraph of endorsements corresponding to a specific skill, and then applying a ranking method admitting weighted digraphs, such as PageRank. We describe the method, and test it on a synthetic network of 1493 nodes, fitted with endorsements.

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“…N. K. Sharma et al, [1] inferred a user's expertise by analysing the meta-data of Twitter Lists features. J. Weng et al, [4] measure influence of users in Twitter creating TwitterRank. It measured influence taking both topical similarity between users and the link structure into account.…”

Section: Literature Reviewmentioning

confidence: 99%

Weighted Factors for Opinion Credibility’s in Twitter

2016

IJCCIE

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Abstract-In sentiment analysing, most researches focus on only opinion classification and summarization of opinion results. However when we classify sentiment information, which information can identify whether this opinion is credible or not? Do we believe result of this topic can be positive if positive results come from unreliable source and negative results come from reliable source in sentiment analysing? For this reason, we propose a method for calculating of opinion credibility based on authors' expert knowledge. In our method we have two components: 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 statics for accuracy of opinion credibility.

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TwitterRank

Cited by 1,414 publications

References 13 publications

Nepotistic relationships in Twitter and their impact on rank prestige algorithms

Nepotistic relationships in Twitter and their impact on rank prestige algorithms

Endorsement deduction and ranking in social networks

Weighted Factors for Opinion Credibility’s in Twitter

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