OLFinder: Finding opinion leaders in online social networks

AleAhmad, Abolfazl; Karisani, Payam; Rahgozar, Maseud; Oroumchian, Farhad

doi:10.1177/0165551515605217

Cited by 61 publications

(30 citation statements)

References 44 publications

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“…al [90] used regression model with lexical diversities in tweets, average number of characters used in tweets, hashtags, user mentions, URLs, followees, positive and negative sentiments to extract influential users from Twitter. Exponential of number of followers was used in [104] to find the popular users from Twitter. Users were ranked using both tweet-based and user-based scores by Francalanci et.…”

Section: Influential Users In Social Networkmentioning

confidence: 99%

Emotion and sentiment analysis from Twitter text

Sailunaz

Alhajj

2019

Journal of Computational Science

260

122

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Section: Influential Users In Social Networkmentioning

confidence: 99%

Emotion and sentiment analysis from Twitter text

Sailunaz

Alhajj

2019

Journal of Computational Science

260

122

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“…(high) WRA [152] limited request FLDA [5] ? (high) Leadership [2] ? (high) Predictive AWI model [151] ?…”

Section: Topical-sensitivementioning

confidence: 99%

“…This algorithm differs from the others in that it is based on multigraphs (i.e., graphs with multiple edges between two nodes), whose nodes are the users, and the edges are based on followers, retweets and mentions among users. An additional topical-sensitive measure is the Competency [2], which ranks the actors according to his ability to post relevant tweets according to hot topics. The topical detection is given by a Latent Dirichlet Allocation (LDA) algorithm, and the topical relevance of each tweet is determined by a Divergence From Randomness retrieval model.…”

Section: Activity Measuresmentioning

confidence: 99%

“…In Section 3 we explain how to obtain the data through the Twitter API, and we present the anatomy of a tweet. In Section 4 we talk about the ambiguity of the influential user's definition, and we Activity Tweet count score [95] O(T ) General activity O(T ) Topical signal [99] Ω(T ) Signal strength [99] O(T ) Effective readers [75] limited request ActivityScore [152] limited request DiscussRank [60] limited request Competency [2] ? (high) IP Influence [111] limited request Popularity FollowerRank [92] O(1) Popularity [2] O(1) Paradoxical discounted [44] limited request Network Score [99] limited request A Score [123] O(T · k) AA Score [123] O(T · k 2 ) AAI Score [123] limited request Action-Reaction [124] O(T · k 2 ) Starrank [66] O(T + n 2.3727 )…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Measuring user influence on Twitter: A survey

Riquelme

González-Cantergiani

2016

Information Processing & Management

355

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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“…Most social networks are scale-free networks [24]. A scale-free network is a network whose degree distribution follows a power law, so the number of nodes in the network that have k connections, denoted P(k), goes for large values of k as P(k) = k −γ , where γ is a constant number (2 < γ < 3) [25]. Hence, in the social network, a popularity measure of a user can be calculated based on the number of in-links of the users as follows [25]:…”

Section: Influential Vector Of a Usermentioning

confidence: 99%

Detecting the Influencer on Social Networks Using Passion Point and Measures of Information Propagation †

et al. 2020

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Influencer marketing is a modern method that uses influential users to approach goal customers easily and quickly. An online social network is a useful platform to detect the most effective influencer for a brand. Thus, we have an issue: how can we extract user data to determine an influencer? In this paper, a model for representing a social network based on users, tags, and the relationships among them, called the SNet model, is presented. A graph-based approach for computing the impact of users and the speed of information propagation, and measuring the favorite brand of a user and sharing the similar brand characteristics, called a passion point, is proposed. Therefore, we consider two main influential measures, including the extent of the influence on other people by the relationships between users and the concern to user’s tags, and the tag propagation through social pulse on the social network. Based on these, the problem of determining the influencer of a specific brand on a social network is solved. The results of this method are used to run the influencer marketing strategy in practice and have obtained positive results.

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OLFinder: Finding opinion leaders in online social networks

Cited by 61 publications

References 44 publications

Emotion and sentiment analysis from Twitter text

Emotion and sentiment analysis from Twitter text

Measuring user influence on Twitter: A survey

Detecting the Influencer on Social Networks Using Passion Point and Measures of Information Propagation †

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