The Tipping Point In Social Networks: Investigating the Mechanism Behind Viral Information Spreading

Singh, Abhishek; Dharamshi, Niraj; Govarthanarajan, Preethi Thimma; Samale, Premal Dattatray; Eirinaki, Magdalini

doi:10.1109/bigdataservice49289.2020.00016

Cited by 3 publications

(1 citation statement)

References 10 publications

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“…The information shown in the table was extracted from the corresponding Users table for each community from the Stack Exchange data dump. Based on the information presented in Table 3, for all three communities the distribution of first four attributes (i.e., user reputation, profile views, upvotes, and downvotes) seems to follow a Furthermore, all three communities show a large increase in the number of users lately (i.e., around 2018 onwards) which we suspect to be due to the tipping point phenomenon (Singh et al 2020).…”

Section: Community Characteristicsmentioning

confidence: 88%

Survival analysis for user disengagement prediction: question-and-answering communities’ case

Firouzjaei

2022

Soc. Netw. Anal. Min.

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We used survival analysis to model user disengagement in three distinct questions-and-answering communities in this work. We used the complete historical data from domains including Politics, Data Science, and Computer Science from Stack Exchange communities from their inception until May 2021, including information about all users who were members of one of these three communities. Furthermore, in formulating the user disengagement prediction as a survival analysis task, we employed two survival analysis techniques (Kaplan–Meier and random survival forests) to model and predicted the probabilities of members of each community becoming disengaged. Our main finding is that the likelihood of users with even a few contributions staying active is noticeably higher than those who were making no contributions; this distinction may widen as time passes. Moreover, the results of our experiments indicate that users with more favourable views toward the content shared on the platform may stay engaged longer. Finally, regardless of their themes, the observed pattern holds for all three communities.

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Section: Community Characteristicsmentioning

confidence: 88%

Survival analysis for user disengagement prediction: question-and-answering communities’ case

Firouzjaei

2022

Soc. Netw. Anal. Min.

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Efficient Approximation Algorithms for Minimum Cost Seed Selection with Probabilistic Coverage Guarantee

Feng,

Chen,

Guo

et al. 2024

Proc. ACM Manag. Data

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Given a social network G , a cost associated with each user, and an influence threshold η, the minimum cost seed selection problem (MCSS) aims to find a set of seeds that minimizes the total cost to reach η users. Existing works are mainly devoted to providing an expected coverage guarantee on reaching η, classified as MCSS-ECG, where their solutions either rely on an impractical influence oracle or cannot attain the expected influence threshold. More importantly, due to the expected coverage guarantee, the actual influence in a campaign may drift from the threshold evidently. Thus, the advertisers would like to request for a probability guarantee of reaching η. This motivates us to further solve the MCSS problem with a probabilistic coverage guarantee, termed MCSS-PCG. In this paper, we first propose our algorithm CLEAR to solve MCSS-ECG, which reaches the expected influence threshold without any influence oracle or influence shortfall but a practical approximation ratio. However, the ratio involves an unknown term (i.e., the optimal cost). Thus, we further devise the STAR method to derive a lower bound of the optimal cost and then obtain the first explicit approximation ratio for MCSS-ECG. In MCSS-PCG, it is necessary to estimate the probability that the current seeds reach η, to decide when to stop seed selection. To achieve this, we design a new technique named MRR, which provides efficient probability estimation with a theoretical guarantee. With MRR in hand, we propose our algorithm SCORE for MCSS-PCG, whose performance guarantee is derived by measuring the gap between MCSS-ECG and MCSS-PCG, and applying the theoretical results in MCSS-ECG. Finally, extensive experiments demonstrate that our algorithms achieve up to two orders of magnitude speed-up compared to alternatives while meeting the requirement of MCSS-PCG with the smallest cost.

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Social Media Virality: Reaching the Tipping Point

Bhimani,

Zainulabdin,

Ali

et al. 2024

JIBRM

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Social media virality has become a key factor in determining how far to extend social media marketing and digital promotion campaigns. The point at which content transitions to viral spread underpins promotion-level decisions. We employ a logistic equation to identify tipping point decisions for viral content online promotions in given contexts. We consider case studies indicative of viral content on social media following a traditional S-curve when examined in a single domain. Our study contributes to understanding the decision mechanics of reaching a tipping point in a manner that reflects the concerns of digital marketers and online marketing strategies generally. We also see our approach to determining this point as furthering social media research in an area of growing importance and assessing how viral content can be managed.

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The Tipping Point In Social Networks: Investigating the Mechanism Behind Viral Information Spreading

Cited by 3 publications

References 10 publications

Survival analysis for user disengagement prediction: question-and-answering communities’ case

Survival analysis for user disengagement prediction: question-and-answering communities’ case

Efficient Approximation Algorithms for Minimum Cost Seed Selection with Probabilistic Coverage Guarantee

Social Media Virality: Reaching the Tipping Point

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