What is the added value of negative links in online social networks?

Kunegis, Jérôme; Preusse, Julia; Schwagereit, Felix

doi:10.1145/2488388.2488452

Cited by 59 publications

(36 citation statements)

References 16 publications

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“…Leskovec et al (2010b) show that signed edges in networks emerge in a manner that is broadly consistent with both of these theories and that socialnetwork structure alone can support accurate edgesign predictions (Leskovec et al, 2010a). Kunegis et al (2013) predict hidden positive and negative edges in a scenario where all observed edges are positive. Bach et al (2013) and frame sign prediction as a hinge-loss Markov random field, a type of probabilistic graphical model introduced by Broecheler et al (2010).…”

Section: Signed-network Analysissupporting

confidence: 53%

Exploiting Social Network Structure for Person-to-Person Sentiment Analysis

West

Paskov

Leskovec

et al. 2014

TACL

153

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Person-to-person evaluations are prevalent in all kinds of discourse and important for establishing reputations, building social bonds, and shaping public opinion. Such evaluations can be analyzed separately using signed social networks and textual sentiment analysis, but this misses the rich interactions between language and social context. To capture such interactions, we develop a model that predicts individual A's opinion of individual B by synthesizing information from the signed social network in which A and B are embedded with sentiment analysis of the evaluative texts relating A to B. We prove that this problem is NP-hard but can be relaxed to an efficiently solvable hinge-loss Markov random field, and we show that this implementation outperforms text-only and network-only versions in two very different datasets involving community-level decision-making: the Wikipedia Requests for Adminship corpus and the Convote U.S. Congressional speech corpus.

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Section: Signed-network Analysissupporting

confidence: 53%

Exploiting Social Network Structure for Person-to-Person Sentiment Analysis

West

Paskov

Leskovec

et al. 2014

TACL

153

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“…Furthermore, the specification of negative links is interesting in its own right. On the other hand, it is generally not very desirable for online social networks to explicitly collect negative links [8,12]. As a consequence, most social media sites such as Facebook and Twitter do not enable users to explicitly specify negative links.…”

Section: Introductionmentioning

confidence: 99%

Negative Link Prediction in Social Media

Tang

Chang

Aggarwal³

et al. 2015

Proceedings of the Eighth ACM International Conference on Web Search and Data Mining

117

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Signed network analysis has attracted increasing attention in recent years. This is in part because research on signed network analysis suggests that negative links have added value in the analytical process. A major impediment in their effective use is that most social media sites do not enable users to specify them explicitly. In other words, a gap exists between the importance of negative links and their availability in real data sets. Therefore, it is natural to explore whether one can predict negative links automatically from the commonly available social network data. In this paper, we investigate the novel problem of negative link prediction with only positive links and content-centric interactions in social media. We make a number of important observations about negative links, and propose a principled framework NeLP, which can exploit positive links and content-centric interactions to predict negative links. Our experimental results on real-world social networks demonstrate that the proposed NeLP framework can accurately predict negative links with positive links and content-centric interactions. Our detailed experiments also illustrate the relative importance of various factors to the effectiveness of the proposed framework.

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“…positive link prediction [Guha et al 2004;Leskovec et al 2010a], and recommender systems [Victor et al 2009;Ma et al 2009]. However, it is generally not very desirable for online social networks to explicitly collect negative links [Hardin 2004;Kunegis et al 2013]. As a consequence, the vast majority of social media sites such as Twitter and Facebook do not enable users to explicitly specify negative links.…”

Section: Promising Directions For Link-oriented Tasksmentioning

confidence: 99%

A Survey of Signed Network Mining in Social Media

et al. 2016

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Many real-world relations can be represented by signed networks with positive and negative links, as a result of which signed network analysis has attracted increasing attention from multiple disciplines. With the increasing prevalence of social media networks, signed network analysis has evolved from developing and measuring theories to mining tasks. In this article, we present a review of mining signed networks in the context of social media and discuss some promising research directions and new frontiers. We begin by giving basic concepts and unique properties and principles of signed networks. Then we classify and review tasks of signed network mining with representative algorithms. We also delineate some tasks that have not been extensively studied with formal definitions and also propose research directions to expand the field of signed network mining.

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What is the added value of negative links in online social networks?

Cited by 59 publications

References 16 publications

Exploiting Social Network Structure for Person-to-Person Sentiment Analysis

Exploiting Social Network Structure for Person-to-Person Sentiment Analysis

Negative Link Prediction in Social Media

A Survey of Signed Network Mining in Social Media

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