Sentiment-based influence detection on Twitter

Bigonha, Carolina A. S.; Cardoso, Thiago N. C.; Moro, Mirella M.; Gonçalves, Marcos André; Almeida, Virgílio A. F.

doi:10.1007/s13173-011-0051-5

Cited by 50 publications

(33 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…These tweets contain a wide variety of information, ranging from conversational tweets to highly relevant information on specific topics [Bigonha et al 2012]. As a result, Twitter has become one of the most important mediums of communication.…”

Section: Identifying Topical Authorities In Microblogsmentioning

confidence: 99%

“…(2) The Followers to Followees ratio (F-F ratio), that is, the number of a user's followers and the number of other people that the user follows (followees). As suggested in [Bigonha et al 2012], [Leavitt et al 2009], this metric valorises users who are widely followed, but have some selection for following others. In fact, the F-F ratio approaches infinity when the number of followers is very high and the number of followees is very low.…”

Section: Identifying Topical Authorities In Microblogsmentioning

confidence: 99%

“…This is the case for authoritative users who are followed by a large number of tweeters but are selective in following others. On the other hand, when the ratio approaches 0, the user might be categorised as a conversationalist, one who follows more users than is followed [Bigonha et al 2012], [Leavitt et al 2009]. (3) The number of retweets, which measures the number of times an author's tweets were retweeted by other users.…”

Section: Identifying Topical Authorities In Microblogsmentioning

confidence: 99%

See 2 more Smart Citations

Identifying Authorities in Online Communities

Bouguessa

Romdhane

2015

ACM Trans. Intell. Syst. Technol.

View full text Add to dashboard Cite

Several approaches have been proposed for the problem of identifying authoritative actors in online communities. However, the majority of existing methods suffer from one or more of the following limitations: (1) there is a lack of an automatic mechanism to formally discriminate between authoritative and nonauthoritative users. In fact, a common approach to authoritative user identification is to provide a ranked list of users expecting authorities to come first. A major problem of such an approach is where to stop reading the ranked list of users? How many users should be chosen as authoritative? (2) supervised learning approaches for authoritative user identification suffer from their dependency on the training data. The problem here is that labeled samples are more difficult, expensive, and time consuming to obtain than unlabeled ones. (3) several approaches rely on some user parameters to estimate an authority score. Detection accuracy of authoritative users can be seriously affected if incorrect values are used. In this paper, we propose a parameterless mixture model-based approach which is capable of addressing the three aforementioned issues in a single framework. In our approach, we first represent each user with a feature vector composed of information related to its social behaviour and activity in an online community. Next, we propose a statistical framework, based on the multivariate beta mixtures, in order to model the estimated set of feature vectors. The probability density function is therefore estimated and the beta component that corresponds to the most authoritative users is identified. The suitability of the proposed approach is illustrated on real data extracted from Stack Exchange question-answering network and Twitter.

show abstract

Section: Identifying Topical Authorities In Microblogsmentioning

confidence: 99%

Section: Identifying Topical Authorities In Microblogsmentioning

confidence: 99%

Section: Identifying Topical Authorities In Microblogsmentioning

confidence: 99%

See 1 more Smart Citation

Identifying Authorities in Online Communities

Bouguessa

Romdhane

2015

ACM Trans. Intell. Syst. Technol.

View full text Add to dashboard Cite

show abstract

“…The more recent literature has associated the complexity of the concept of influence with the diversity of content. Several research works have addressed the need for considering content-based metrics of influence (Bakshy et al 2011;Bigonha et al 2012;Hossain et al 2006;Li et al 2014;Naaman et al 2010). …”

Section: State Of the Artmentioning

confidence: 99%

A Visual Analysis of Social Influencers and Influence in the Tourism Domain

Francalanci

Hussain

2014

Information and Communication Technologies in Tourism 2015

View full text Add to dashboard Cite

Identifying influencers is an important step towards understanding how information spreads within a network. In social media, hub nodes are generally considered as social influencers. Social networks follow a power-law degree distribution of nodes, with a few hub nodes and a long tail of peripheral nodes. While there exist consolidated approaches supporting the identification and characterization of hub nodes, research on the analysis of the multi-layered distribution of peripheral nodes is limited. However, influence seems to spread following multihop paths across nodes in peripheral network layers. This paper proposes a visual approach to the graphical representation and exploration of peripheral layers by exploiting the theory of k-shell decomposition analysis. We put forward three hypotheses that allow the graphical identification of peripheral nodes that are more likely to be influential and contribute to the spread of information. Hypotheses are tested on a large sample of tweets from the tourism domain.

show abstract

“…Bigonha et al [3] found that the readability quality of a message was useful (along with network features ) in identifying the most influential members of a network. This stream of work has also been applied outside Twitter.…”

Section: Related Workmentioning

confidence: 99%

Sonora: A Prescriptive Model for Message Authoring on Twitter

Mendes¹,

Gruhl²,

Drews³

et al. 2014

Web Information Systems Engineering – WISE 2014

View full text Add to dashboard Cite

Abstract. Within social networks, certain messages propagate with more ease or attract more attention than others. This effect can be a consequence of several factors, such as topic of the message, number of followers, real-time relevance, person who is sending the message etc. Only one of these factors is within a user's reach at authoring time: how to phrase the message. In this paper we examine how word choice contributes to the propagation of a message.We present a prescriptive model that analyzes words based on their historic performance in retweets in order to propose enhancements in future tweet performance. Our model calculates a novel score (SONORA SCORE) that is built on three aspects of diffusion -volume, prevalence and sustain.We show that SONORA SCORE has powerful predictive ability, and that it complements social and tweet-level features to achieve an F1 score of 0.82 in retweet prediction. Moreover, it has the ability to prescribe changes to the tweet wording such that when the SONORA SCORE for a tweet is higher, it is twice as likely to have more retweets.Lastly, we show how our prescriptive model can be used to assist users in content creation for optimized success on social media. Because the model works at the word level, it lends itself extremely well to the creation of user interfaces which help authors incrementally -word by word -refine their message until its potential is maximized and it is ready for publication. We present an easy to use iOS application that illustrates the potential of incremental refinement using SONORA SCORE coupled with the familiarity of a traditional spell checker.

show abstract

Sentiment-based influence detection on Twitter

Cited by 50 publications

References 20 publications

Identifying Authorities in Online Communities

Identifying Authorities in Online Communities

A Visual Analysis of Social Influencers and Influence in the Tourism Domain

Sonora: A Prescriptive Model for Message Authoring on Twitter

Contact Info

Product

Resources

About