Impact and degree of user sociability in social media

Lee, Jeehoon

doi:10.1016/j.ins.2012.01.040

Cited by 12 publications

(7 citation statements)

References 18 publications

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“…Grabowicz et al in [57] analyzed the relationship between SM links and real-world tie-strength and Pan et al in [58] attempted to quantify the role of tie-strength plays in scientific collaboration networks. Shin et al presented a method to quantify the degree of user sociability in SM relying on the tie-strength [59].…”

Section: Introductionmentioning

confidence: 99%

CARESOME: A system to enrich marketing customers acquisition and retention campaigns using social media information

Bernabé-Moreno

Tejeda-Lorente

Porcel

et al. 2015

Knowledge-Based Systems

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Section: Introductionmentioning

confidence: 99%

CARESOME: A system to enrich marketing customers acquisition and retention campaigns using social media information

Bernabé-Moreno

Tejeda-Lorente

Porcel

et al. 2015

Knowledge-Based Systems

View full text Add to dashboard Cite

“…They are more frequently mentioned but do not actively repost or reply to their followers. Weibo measures a user’s influence by one-way searching or following, neglecting the user’s sociability, even though sociability should also be considered a significant factor in a user’s level and the subsequent ranking of the user’s content ( Shin and Lee, 2012 ). For our study, when we did not distinguish between influencers and ordinary users, interactivity had no significant effect on collective anxiety.…”

Section: Discussionmentioning

confidence: 99%

Influential Factors on Collective Anxiety of Online Topic-Based Communities

Yang

et al. 2021

Front. Psychol.

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Background: Under the uncertainty led by the decentralized information on social media, people seek homogeneity in either opinions or affection to establish group identity to better understand the information. This also means they are easily polarized, not only ideologically but also in their actions. Affective polarization is the emotional tendency for people to show animosity toward opposing partisans while seeking homogeneity from fellow partisans. Much research into online affective polarization has focused on quantifying anxiety at an individual level while neglecting that on a collective basis. Therefore, this paper examined the polarization of collective anxiety in topic-based communities on Weibo.Methods: We aim to interpret correlations between collective anxiety online and topic characteristics, user competence, as well as the proportion of influencers of Weibo topic-based communities. Our neural networks model and statistical analysis were based on 200 communities with 403,380 personal accounts and 1,012,830 messages.Results: Collective anxiety levels are correlated to (1) the extent to which a topic captures public interest, (2) how community members articulate topics on social network platforms, and (3) the ratio of influencers in the community. Specifically, people’s conflicting perceptions and articulations of topics might increase collective anxiety, while the extent to which a topic is of the public interest and the number of influencers engaged in a topic account for any decline in its ranking. Furthermore, familiarity with a topic does not help predict collective anxiety levels. There are no significant links between community size or interactivity dynamics and the level of collective anxiety in the topic-based community. Our computational model has 85.00% precision and 87.00% recall.Conclusion: This study found the collective anxiety augment due to topic proximities to public interest and members’ lack of declarative knowledge on topics, while to decline with an increasing portion of online influencers. These findings indicate that collective anxiety is induced due to a lack of credibility. Also, the amount of conflicting information shared by different people places them in a state of flux. Therefore, a community with more influencers may be more likely to experience anxiety polarization, bringing forth the issue of layered information and inequality.

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“…Users are ranked on their scores to find the top-N opinion leaders. In [11], to obtain the user rank, new schemes were proposed to separately and effectively estimate reputation. The SuperedgeRank algorithm [12] uses the node superdegree, superedge degree, superedge-superedge distance, and superedge overlap based on the supernetwork theory to rank superedges and identify opinion leaders.…”

Section: Related Work a Opinion Leaders Mining Algorithmsmentioning

confidence: 99%

An Opinion-Leader Mining Method in Social Networks With a Phased-Clustering Perspective

et al. 2020

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Opinion-leader mining in social networks is a critical problem in research of the information dissemination process and in public opinion guidance and supervision. Not every social network user has a high probability to be an opinion leader. However, most mining methods identify opinion leaders among users in the whole network, which adds unnecessary calculations. To solve this problem, we propose a rank after clustering (RaC) algorithm to mine opinion leaders in social networks with a phased-clustering perspective, which has the following aspects: (1) Aiming to reduce the scale of calculation, the clustering stage clusters users in social networks using a K-means algorithm according to topological information to find the set of opinion leader candidates; (2) The ranking stage determines the user ranks of opinion leader candidates by both their activeness and influence, and we accumulate the followers' influence weighted by degree of attention when assessing user influence. In experiments, a new indicator, the C-value, and simulations based on the linear threshold model are used to evaluate the performance of the RaC algorithm. The results show that RaC is effective and accurate.

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Impact and degree of user sociability in social media

Cited by 12 publications

References 18 publications

CARESOME: A system to enrich marketing customers acquisition and retention campaigns using social media information

CARESOME: A system to enrich marketing customers acquisition and retention campaigns using social media information

Influential Factors on Collective Anxiety of Online Topic-Based Communities

An Opinion-Leader Mining Method in Social Networks With a Phased-Clustering Perspective

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