Modelling Emotion Dynamics on Twitter via Hidden Markov Model

Naskar, Debashis; Onaindía, Eva; Rebollo, Miguel; Das, Subhashis

doi:10.1145/3366030.3366092

Cited by 7 publications

(8 citation statements)

References 14 publications

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“…e subsequent negative online public opinion is very likely to induce the unhealthy emotions of the people which lead to violations or excessive behaviours, which seriously affect social stability [4]. With the advent of the Web 2.0 era, the popularization of information technology has changed people's thoughts and behaviours and, at the same time, has brought new challenges to the management of social public affairs [5]. From the perspective of online public opinion, with the advancement of the democratization of the country's society, the public's awareness of participation in politics is increasing, and the cognitive tendency of internet users on current policies can feed back a lot of hidden information, which promotes the formulation and evaluation of national public policies [6].…”

Section: Introductionmentioning

confidence: 99%

[Retracted] Forecast and Simulation of the Public Opinion on the Public Policy Based on the Markov Model

2021

Complexity

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Public policy and public opinion directly affect the image of the government, but due to the lack of appropriate monitoring and early warning tools, the government’s handling of credit changes is seriously lagging behind. In response to this problem, this paper integrates the internet, public information, market credit information, and other data, uses hidden Markov models and natural language processing technology, and establishes a modern government public policy and public opinion monitoring and early warning model to evaluate government credit in real time; the government can formulate relevant policies based on the evaluation results to improve the government’s governance capabilities. Empirical analysis shows that, based on the dynamic scoring framework and Markov model, the government credit monitoring and early warning models established, respectively, have 90% of the reference value, and the analysis results have the same reference. This method can effectively predict the trend of hot online public opinion. The subsequent establishment of an online public opinion early warning system and an online public opinion guidance mechanism provided theoretical support.

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Section: Introductionmentioning

confidence: 99%

[Retracted] Forecast and Simulation of the Public Opinion on the Public Policy Based on the Markov Model

2021

Complexity

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“…It is worth noting that the unobtrusive access to time-sensitive and emotion-loaded information on social media brings up new opportunities for emotional analysis with an emphasis on the dynamic nature of human sentiments (Naskar et al, 2019; Seabrook et al, 2018). In recent years, there is an increasing interest in developing innovative analytical tools to discover the mechanisms of netizens’ emotion dynamics towards social events (Naskar et al, 2019). For instance, Seabrook et al (2018) conducted a longitudinal analysis of the language-based emotion dynamics of UGC to predict depression risks in netizens.…”

Section: Literature Reviewmentioning

confidence: 99%

Social Media and Disaster Risk Reduction and Management: How Have Reddit Travel Communities Experienced the COVID-19 Pandemic?

Hao

Park

Chon

2022

Journal of Hospitality & Tourism Research

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Social media plays an important role in disaster risk reduction and management. This study brings text, emotion, and timeline analyses together to increase our understanding of online travel community members’ emotional dynamics and meaningful themes of concerns during the early months of the COVID-19 pandemic. Qualitative data was collected from three online travel communities on Reddit. We found that, as the pandemic continued, Redditors’ concerns shifted from context-related external issues to daily-life-related internal issues. Furthermore, group-based emotions formed by the virtual community evolved positively, which can be beneficial for restoring confidence in travel after the pandemic. After presenting the results, we discuss their theoretical and practical implications.

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“…NBA's playoff or NCAA's March Madness), while less polarized during off-season. In the literature, [31] models Twitter users' emotion dynamics as a Markov process. [8] characterizes the pattern of Twitter users' retweets during the presidential election using a Markov bridge model.…”

Section: Edge Formation Protocol With Markovian Acceptance Probabilitymentioning

confidence: 99%

Controlling Segregation in Social Network Dynamics as an Edge Formation Game

Luo¹,

Nettasinghe²,

Krishnamurthy³

2021

Preprint

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This paper studies controlling segregation in social networks via exogenous incentives. We construct an edge formation game on a directed graph. A user (node) chooses the probability with which it forms an inter-or intra-community edge based on a utility function that reflects the tradeoff between homophily (preference to connect with individuals that belong to the same group) and the preference to obtain an exogenous incentive. Decisions made by the users to connect with each other determine the evolution of the social network. We explore an algorithmic recommendation mechanism where the exogenous incentive in the utility function is based on weak ties which incentivizes users to connect across communities and mitigates the segregation. This setting leads to a submodular game with a unique Nash equilibrium. In numerical simulations, we explore how the proposed model can be useful in controlling segregation and echo chambers in social networks under various settings.

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Modelling Emotion Dynamics on Twitter via Hidden Markov Model

Cited by 7 publications

References 14 publications

[Retracted] Forecast and Simulation of the Public Opinion on the Public Policy Based on the Markov Model

[Retracted] Forecast and Simulation of the Public Opinion on the Public Policy Based on the Markov Model

Social Media and Disaster Risk Reduction and Management: How Have Reddit Travel Communities Experienced the COVID-19 Pandemic?

Controlling Segregation in Social Network Dynamics as an Edge Formation Game

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