Information diffusion in online social networks

Guille, Adrien; Hacid, Hakim; Favre, Cécile; Zighed, Djamel A.

doi:10.1145/2503792.2503797

Cited by 759 publications

(412 citation statements)

References 40 publications

(8 reference statements)

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“…The calculation of the parameter values using B for the information diffusion process model is shown in Equation (2). b im is obtained from the frequency of community c i , which is paired with a topic t m , divided by the total frequency of all topics paired with c i .…”

Section: Semantic Information Flowmentioning

confidence: 99%

“…There have been several studies on information flow modeling based on the structure of social network or the discovery of information diffusion processes without analyzing the structure of There have been several studies on information flow modeling based on the structure of social network or the discovery of information diffusion processes without analyzing the structure of communication [1][2][3][4][5][6]. However, their studies have not shown the relation between context and information flow.…”

Section: Introductionmentioning

confidence: 99%

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SentiFlow: An Information Diffusion Process Discovery Based on Topic and Sentiment from Online Social Networks

Carrera

Jung

2018

Sustainability

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Abstract:In this digital era, people can become more interconnected as information spreads easily and quickly through online social media. The rapid growth of the social network services (SNS) increases the need for better methodologies for comprehending the semantics among the SNS users. This need motivated the proposal of a novel framework for understanding information diffusion process and the semantics of user comments, called SentiFlow. In this paper, we present a probabilistic approach to discover an information diffusion process based on an extended hidden Markov model (HMM) by analyzing the users and comments from posts on social media. A probabilistic dissemination of information among user communities is reflected after discovering topics and sentiments from the user comments. Specifically, the proposed method makes the groups of users based on their interaction on social networks using Louvain modularity from SNS logs. User comments are then analyzed to find different sentiments toward a subject such as news in social networks. Moreover, the proposed method is based on the latent Dirichlet allocation for topic discovery and the naïve Bayes classifier for sentiment analysis. Finally, an example using Facebook data demonstrates the practical value of SentiFlow in real world applications.

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Section: Semantic Information Flowmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

SentiFlow: An Information Diffusion Process Discovery Based on Topic and Sentiment from Online Social Networks

Carrera

Jung

2018

Sustainability

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“…Generally, OSN are represented by graphs in which nodes act as users and edges describes relationships [1]. In OSN exchange of information is growing rapidly and Copyright ⓒ 2017 SERSC various social media like Facebook, Twitter, and LinkedIn etc.…”

Section: Information Diffusionmentioning

confidence: 99%

Current Trends in Text Mining for Social Media

Singh¹,

Kumari²,

Pal³

et al. 2017

IJGDC

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“…This means that information diffusion is often limited to the immediate neighbours (or n-hops neighbours, with small values of n) of the user who generated the content. Thus, identifying influential nodes covering the role of opinion leaders able to generate large diffusions, and detecting popular topics are important tasks for the analysis and prediction of information diffusion [20]. Based on these remarks, several models have been proposed to simulate information diffusion in OSN [14,17,28,32].…”

Section: Information Diffusion Analysis In Osnmentioning

confidence: 99%

Information diffusion in distributed OSN: The impact of trusted relationships

Arnaboldi

Gala

Passarella

et al. 2015

Peer-to-Peer Netw. Appl.

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Distributed Online Social Networks (DOSN) are a valid alternative to OSN based on peer-to-peer communications. Without centralised data management, DOSN must provide the users with higher level of control over their personal information and privacy. Thus, users may wish to restrict their personal network to a limited set of peers, depending on the level of trust with them. This means that the effective social network (used for information exchange) may be a subset of the complete social network, and may present different structural patterns, which could limit information diffusion. In this paper, we estimate the capability of DOSN to diffuse content based on trust between social peers. To have a realistic representation of a OSN friendship graph, we consider a large-scale Facebook network, from which we estimate the trust level between friends. Then, we consider only social links above a certain threshold of trust, and we analyse the potential capability of the resulting graph to spread information through several structural indices. We test four possible thresholds, coinciding with the definition of personal social circles derived from sociology and anthropology. The results show that limiting the network to "active social contacts" leads to a graph with high network connectivity, where the nodes are still wellconnected to each other, thus information can potentially cover a large number of nodes with respect to the original graph. On the other hand, the coverage drops for more restrictive assumptions. Nevertheless the re-insertion of a single excluded friend for each user is sufficient to obtain good coverage (i.e., always higher than 40 %) even in the most restricted graphs. We also analyse the potential capability of the network to spread information (i.e., network spreadability), studying the properties of the social paths between any pairs of users in the graph, which represent the effective channels traversed by information. The value of contact frequency between pairs of users determines a decay of trust along the path (the higher the contact frequency the lower the decay), and a consequent decay in the level of trustworthiness of information traversing the path. We show that selecting the link to re-insert in the network with probability proportional to its level of trust is the best re-insertion strategy, as it leads to the best connectivity/spreadability combination.

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Information diffusion in online social networks

Cited by 759 publications

References 40 publications

SentiFlow: An Information Diffusion Process Discovery Based on Topic and Sentiment from Online Social Networks

SentiFlow: An Information Diffusion Process Discovery Based on Topic and Sentiment from Online Social Networks

Current Trends in Text Mining for Social Media

Information diffusion in distributed OSN: The impact of trusted relationships

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