Exploring celebrity dynamics on Twitter

Srinivasan, Mahesh; Srinivasa, Srinath; Thulasidasan, Sunil

doi:10.1145/2528228.2528242

Cited by 14 publications

(10 citation statements)

References 8 publications

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“…Furthermore, there exist measures that use several kind of metrics, which are well correlated with other measures that only use a strict subset of those metrics. Such is the case of the AAI measure, for instance, that is well correlated with the number of followers [123]. The experiments of Section 8.3 suggest that the FollowerRank and the A Score could be another example.…”

Section: Open Research Problemsmentioning

confidence: 95%

“…Other relevant users are the celebrities [123], who meet somewhat different criteria from those of influencers. Users can be classified according to their popularity into broadcasters or passive users [109] (many followers and few followees), acquaintances (similar amount of followers and followees) and miscreants or evangelists (few followers and many followees, like spammers and bots) [71].…”

Section: What Is An Influential User?mentioning

confidence: 99%

“…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 2 more Smart Citations

Measuring user influence on Twitter: A survey

Riquelme

González-Cantergiani

2016

Information Processing & Management

373

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: Open Research Problemsmentioning

confidence: 95%

Section: What Is An Influential User?mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Measuring user influence on Twitter: A survey

Riquelme

González-Cantergiani

2016

Information Processing & Management

373

230

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“…Acquaintance Affinity Identification(AAI) score was calculated with the number of followers, number of users mentioning the author, number of users replied to that author's tweets, number of users retweeted the author's tweets [98]. Various common centrality measures like betweenness and closeness centrality were used by researchers to find influential users from a network [99], whereas few researchers found their own centrality indexes like H-index which considered a maximum h value for replies, retweets, likes for the calculations [100,101].…”

Section: Influential Users In Social Networkmentioning

confidence: 99%

Emotion and sentiment analysis from Twitter text

Sailunaz

Alhajj

2019

Journal of Computational Science

299

122

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“…The irst group of inluencers are often referred to as opinion leaders [22], prestigious innovators [13], key-players [8] and spreaders [32]. The second group of inluencers are often referred to as celebrities [52], evangelists [4] or experts [28], such as a journalist at BBC or a professor at Harvard.…”

Section: Influencersmentioning

confidence: 99%

Influencers in Multiplayer Online Shooters

Canossa¹,

Azadvar²,

Harteveld

et al. 2019

Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems

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In a wide range of social networks, people's behavior is inluenced by social contagion: we do what our network does. Networks often feature particularly inluential individuals, commonly called łinluencers'. ' Existing work suggests that in-game social networks in online games are similar to reallife social networks in many respects. However, we do not know whether there are in-game equivalents to inluencers. We therefore applied standard social network features used to identify inluencers to the online multiplayer shooter Tom Clancy's The Division. Results show that network featuredeined inluencers had indeed an outsized impact on playtime and social play of players joining their in-game network.

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Exploring celebrity dynamics on Twitter

Cited by 14 publications

References 8 publications

Measuring user influence on Twitter: A survey

Measuring user influence on Twitter: A survey

Emotion and sentiment analysis from Twitter text

Influencers in Multiplayer Online Shooters

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