Extracting Social Networks to Understand Interaction

Forestier, Mathilde; Velcin, Julien; Zighed, Djamel A.

doi:10.1109/asonam.2011.64

Cited by 12 publications

(18 citation statements)

References 12 publications

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“…We actually identified that there is an issue with the algorithm that extracts the name citations [16]. For example, a user who uses the pseudonym "obamaa1" will be identified as a celebrity because of the name "Obama" mentioned several times inside the comments of the forum.…”

Section: Discussionmentioning

confidence: 99%

“…The parser aims at extracting the user posts, the user's pseudonym and the structural relation, i.e., the relation based on the "reply to" structure of the forums. We, then, extract the citation relations, i.e., when a user cites another user within his post and the quotation relations, i.e., when a user quotes a part of a previous post in his post [16]. All this information is saved into a database.…”

Section: Characteristicsmentioning

confidence: 99%

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Extracting Celebrities from Online Discussions

Forestier¹,

Velcin²,

Stavrianou

et al. 2012

2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining

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Online discussions became increasingly widespread with the Web 2.0: no matter the distance, whether you know the person or not, you can discuss and exchange ideas with people all over the world through forums, blogs, and newsgroups. The news websites have extensively used forums in order to encourage the reader being a real participant in the information media. This paper aims at automatically extracting the celebrities from such discussions. We propose certain meta-criteria and we provide an evaluation on a dataset of 35,175 posts written by 14,443 users. The results show that one of the proposed meta-criteria succeeds in extracting celebrities and allows for further improvements. I. INTRODUCTIONThe Web 2.0 has introduced an incredibly simple way of interaction: regardless the distance or whether you know the person or not, you can discuss with people from all over the world through the Web 2.0 applications (blogs, e-mails, dedicated media, etc.). In particular, forum debates on news websites are a very representative case of interaction between people using the Web 2.0.In this kind of interaction, as in real life, people play a social role [1]. Goffman [2] defines the social role as "the enactment of rights and duties attached to a given status". In other words, people have some regularities in their behaviour and this behaviour can be analysed in order to figure out the social role. Golder and Donath [3] study the Usenet newsgroup and they define the celebrity social role in order to represent people who are recognised in and by their community.As a result, in this paper we provide three major contributions: the theoretical formalisation of the 'celebrity' social role inspired by and based on previous anthropological studies; the experiments of this theoretical framework on data extracted from a news website using three different meta-criteria and a baseline-criterion; and a discussion about the social role extraction, and the relation with the kind of data we deal with.The paper continues as follows. At first, we discuss the related work concerning the topic of social role recognition in online media. Then, we present the theoretical framework based on the study of Golder and Donath [3] extended with three meta-criteria to take into account the special features of the forum debates on news websites. We continue by presenting the experimental framework and the dataset we used, and we discuss about the evaluation of the different criteria applied. We conclude by commenting on the results and discussing the outcome.

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Section: Discussionmentioning

confidence: 99%

Section: Characteristicsmentioning

confidence: 99%

Extracting Celebrities from Online Discussions

Forestier¹,

Velcin²,

Stavrianou

et al. 2012

2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining

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“…The analysis of web forum and news group interactions has been well researched for many decades, and more recently these ideas have been migrated to the field of social media network analysis [1].…”

Section: Related Workmentioning

confidence: 99%

Knowing a Good Show When You See One

Lanagan

2012

2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining

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Social media has become an integral part of the web, and its popularity continues to provide an outlet for people's opinions and discussion about any topic of interest. In this paper we examine the interest around a number of television series that are broadcast on a weekly basis. Our aim is to show that by observing solely the initial interactions of fans or users of a web forum, we can extrapolate the longer-term interest in particular episodes. We do so by observing the temporal dynamics of the conversation, and performing a clustering so as to judge how much time is required before reasonable conclusions can be drawn about the level of interest surrounding an episode. We find that early interaction trends have strong similarities with the overall conversation patterns.

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“…Multidimensional approaches like the work of Brodka et al [14] where they define a multi-layer social network as a set of single-layered social graphs with fixed set of nodes and variation on their edges, the work of M. Magnani and L.Rossi [15] where was proposed a multidimensional model where each layer corresponded to an OSN and a node mapping function between those layers, the work of A. Socievole et al [13] where was presented a model with a routing protocol that makes use of multi-layer social networks to select nodes in order to act as message relays, or even the work of Forestier et al [16] where was proposed a multidimensional social network from online discussions with relations derived from structure and text content.…”

Section: Multidimensional Networkmentioning

confidence: 99%

Multidimensional Approach to Online Interest Networks

Barbosa¹,

Santos²

2017

dtcse

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Abstract. Online social networks allow users to socialise with others and also construct and manage their identities via self-presentation. This creates a network of interests that is often represented by a single network that aggregates all of the connections, however it is possible to increase the possibilities by dividing it into layers of individual networks. In this paper we propose a model for a multidimensional network of interests based on the interests that online social network users provide on their profiles. This multidimensional approach can be useful for getting insights about user behaviour or even help organisations understand the impact of their online content.

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Extracting Social Networks to Understand Interaction

Cited by 12 publications

References 12 publications

Extracting Celebrities from Online Discussions

Extracting Celebrities from Online Discussions

Knowing a Good Show When You See One

Multidimensional Approach to Online Interest Networks

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