Detecting Real-World Influence through Twitter

Cossu, Jean-Valère; Dugué, Nicolas; Labatut, Vincent

doi:10.1109/enic.2015.20

Cited by 39 publications

(22 citation statements)

References 30 publications

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“…The influential users tend to be active [20,66], but only a few active users are influential. Furthermore, it is important to understand that influential users in a social network are not neccesarily influential in the real life [24,25].…”

Section: Influence Measuresmentioning

confidence: 99%

Measuring user influence on Twitter: A survey

Riquelme

González-Cantergiani

2016

Information Processing & Management

366

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: Influence Measuresmentioning

confidence: 99%

Measuring user influence on Twitter: A survey

Riquelme

González-Cantergiani

2016

Information Processing & Management

366

230

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“…In certain studies, the authors define it instead as a Boolean feature, depending on whether or not the geolocation options is enabled in the user's profile [73]. Others prefer to count the number of distinct locations associated to the user [18,37]. Like Features 1-5, this feature can help discriminating certain types of users aiming at exhibiting a very complete and controlled image, or with a specific behavior implying the publicization of their physical location (e.g.…”

Section: Publishing Activitymentioning

confidence: 99%

“…Many authors use unigram weighting (i.e. 1-grams, or single words) to take advantage of the tweets content, either by itself [18] or in combination with other features [17,61,64,73,78]. Other authors also focus on bigrams (2-grams, or pairs of words) [1,61,64,73], for which the same weighting schemes can be applied than for unigrams.…”

Section: Lexical Aspectsmentioning

confidence: 99%

“…Other authors also focus on bigrams (2-grams, or pairs of words) [1,61,64,73], for which the same weighting schemes can be applied than for unigrams. But it is also possible to define new ones, for instance by taking advantage of the cooccurrence graphs one can build from bigrams [18] (more details on this in Section 3.3).…”

Section: Lexical Aspectsmentioning

confidence: 99%

“…Acknowledgements This work is a revised and extended version of the article Detecting Real-World Influence Through Twitter, presented at the 2 nd European Network Intelligence Conference (ENIC 2015) by the same authors [18]. It was partly funded by the French National Research Agency (ANR), through the project ImagiWeb ANR-2012-CORD-002-01.…”

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confidence: 99%

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A review of features for the discrimination of twitter users: application to the prediction of offline influence

Cossu

Labatut

Dugué

2016

Soc. Netw. Anal. Min.

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Many works related to Twitter aim at characterizing its users in some way: role on the service (spammers, bots, organizations, etc.), nature of the user (socio-professional category, age, etc.), topics of interest, and others. However, for a given user classification problem, it is very difficult to select a set of appropriate features, because the many features described in the literature are very heterogeneous, with name overlaps and collisions, and numerous very close variants. In this article, we review a wide range of such features. In order to present a clear state-of-the-art description, we unify their names, definitions and relationships, and we propose a new, neutral, typology. We then illustrate the interest of our review by applying a selection of these features to the offline influence detection problem. This task consists in identifying users which are influential in real-life, based on their Twitter account and related data. We show that most features deemed efficient to predict online influence, such as the numbers of retweets and followers, are not relevant to this problem. However, we propose several content-based approaches to label Twitter users as Influencers or not. We also rank them according to a predicted influence level. Our proposals are evaluated over the CLEF RepLab 2014 dataset, and outmatch state-of-the-art methods.

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