A novel information diffusion model for online social networks

AlSuwaidan, Lulwah; Ykhlef, Mourad

doi:10.1145/3151759.3151812

Cited by 4 publications

(2 citation statements)

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“…The role of topicality in Twitter adoption has been considered via machine learning predictive models [ 22 ] where topics correspond to selected hashtags, discovering that topicality plays a major role at microscopic information propagation. Hashtag topics are also used in the construction of the similarity measure underlying a radiation transfer model for influence prediction [ 5 ], but their role is not isolated.…”

Section: Related Workmentioning

confidence: 99%

“…Machine learning prediction F 1 = 0.93 Twitter hashtags and URLs 2009 [ 79 ]/2016 Topic-level SIR model 0.52–0.75 and 0.44–0.79 Yahoo! Finance Walmart message board (139,062 threads, 441,954 messages, 25,500 authors) and US Politics Online Breaking News in Politics (2192 threads, 130,850 messages, 1124 authors) [ 59 ]/2016 SIR model with stifling and forgetting mechanisms N/A Synthetic data and on OSN Renren (9590 nodes, 89,873 edges) [ 26 ]/2017 Hydrodynamic information diffusion prediction model : 76.2–88 6500 video tweets from Sina-weibo [ 5 ]/2017 Physical radiation transfer N/A Twitter dataset about 9000 users [ 40 ]/2017 Decision payoff modeling Avg. precision: 0.7 Sina Weibo and Flickr datasets [ 58 ] Expectation maximizacion.…”

Section: Related Workmentioning

confidence: 99%

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Neuro-semantic prediction of user decisions to contribute content to online social networks

Cleveland

Ríos

Aguilera

et al. 2022

Neural Comput & Applic

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Understanding at microscopic level the generation of contents in an online social network (OSN) is highly desirable for an improved management of the OSN and the prevention of undesirable phenomena, such as online harassment. Content generation, i.e., the decision to post a contributed content in the OSN, can be modeled by neurophysiological approaches on the basis of unbiased semantic analysis of the contents already published in the OSN. This paper proposes a neuro-semantic model composed of (1) an extended leaky competing accumulator (ELCA) as the neural architecture implementing the user concurrent decision process to generate content in a conversation thread of a virtual community of practice, and (2) a semantic modeling based on the topic analysis carried out by a latent Dirichlet allocation (LDA) of both users and conversation threads. We use the similarity between the user and thread semantic representations to built up the model of the interest of the user in the thread contents as the stimulus to contribute content in the thread. The semantic interest of users in discussion threads are the external inputs for the ELCA, i.e., the external value assigned to each choice.. We demonstrate the approach on a dataset extracted from a real life web forum devoted to fans of tinkering with musical instruments and related devices. The neuro-semantic model achieves high performance predicting the content posting decisions (average F score 0.61) improving greatly over well known machine learning approaches, namely random forest and support vector machines (average F scores 0.19 and 0.21).

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Section: Related Workmentioning

confidence: 99%