Interventions in Social Networks: Impact on Mood and Network Dynamics

Greetham, Danica Vukadinović; Sengupta, Abhijit; Hurling, Robert; Wilkinson, Joann

doi:10.1142/s0219525915500162

Cited by 4 publications

(6 citation statements)

References 34 publications

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“…The level of collective effervescence and the corresponding individual experience of emotional energy are accordingly mediated by who is participating in the ritual. Such a compositional effect is consistent with the homophily principle, which holds that many group similarities, including plausibly emotional moods (Greetham et al 2015), are a result of individuals associating with similar others (McPherson, Smith-Lovin, and Cook 2001).…”

supporting

confidence: 80%

Exploring the Sources of Collective Effervescence: A Multilevel Study

Liebst¹

2019

SocScience

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Collective effervescence is assigned a key role in sociological theorizing on ritual and group processes, yet surprisingly little research has systematically measured the phenomenon and examined its sources. In addressing this research gap, the current article explores and compares several correlates of collective effervescence. The data included questionnaires and geospatial records of spatial setting and movement patterns recorded at a large music festival. Multilevel regression modeling was applied, and the strength of the estimated evidence was assessed with frequentist and Bayesian approaches. Results suggest that collective effervescence is a highly spatially clustered phenomenon that, in particular, is associated with the social-morphological feature of being in a crowd of people. The article discusses the implications of these results for sociological Durkheim scholarship as well as for festival-event studies.

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supporting

confidence: 80%

Exploring the Sources of Collective Effervescence: A Multilevel Study

Liebst¹

2019

SocScience

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“…We use dynamic communicability, a centrality measure for evolving networks, to assign broadcast scores to users; these scores are one method of quantifying communication reach that has been investigated in the literature. Our investigation is motivated by the finding, in three small observed social network studies [9], that the individuals with large broadcast scores, in general, had very low levels of negative affect at the beginning of the studies.…”

Section: Communicability and Sentimentmentioning

confidence: 99%

“…It has been shown in [7] that positive and negative affects [8] network. Furthermore, it was conjectured in [9] that the people with the potentially largest reach to all the others in a smaller social network over a week belong to the group with the smallest negative affect at the beginning of that period. In this work, we investigate whether similar conclusions can be discovered for large online social networks, using automatic sentiment detection algorithms, and to what extent we can develop a good model of collective sentiments dynamics.…”

Section: Introductionmentioning

confidence: 99%

In the mood: the dynamics of collective sentiments on Twitter

2016

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We study the relationship between the sentiment levels of Twitter users and the evolving network structure that the users created by @-mentioning each other. We use a large dataset of tweets to which we apply three sentiment scoring algorithms, including the open source SentiStrength program. Specifically we make three contributions. Firstly, we find that people who have potentially the largest communication reach (according to a dynamic centrality measure) use sentiment differently than the average user: for example, they use positive sentiment more often and negative sentiment less often. Secondly, we find that when we follow structurally stable Twitter communities over a period of months, their sentiment levels are also stable, and sudden changes in community sentiment from one day to the next can in most cases be traced to external events affecting the community. Thirdly, based on our findings, we create and calibrate a simple agent-based model that is capable of reproducing measures of emotive response comparable with those obtained from our empirical dataset.

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“…Under the names of popular forms of sentiment [15], aggregate mood [16], collective mood [17], collective sentiment [18], background sentiment [13], etc., two types of mass mood studies can be distinguished: (1) where mood is analyzed in terms of positive and negative polarity, and the mood dynamics are related to the movement between the poles [18][19][20][21]; (2) where mood is studied as a set of feelings involving more than one emotion [22], and the mood dynamics are seen as changes in the corresponding emotions [23]. In such approaches, mood does not differ from emotion in duration and is labeled as a rapidly changing attribute [16] with a minute [24], hourly [25], daily, or-at best-weekly cycle of change [26][27][28].…”

Section: Related Workmentioning

confidence: 99%

“…. ., k, corresponds to the data seasonality on dataset D. Then, if the white-to-black ratio BW in Equation ( 2) for T satisfy one of Conditions (17) or (18), we have…”

Section: Cluster Propertiesmentioning

confidence: 99%

Modeling Seasonality of Emotional Tension in Social Media

Nosov,

Kuznetsova,

Stankevich

et al. 2023

Computers

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Social media has become an almost unlimited resource for studying social processes. Seasonality is a phenomenon that significantly affects many physical and mental states. Modeling collective emotional seasonal changes is a challenging task for the technical, social, and humanities sciences. This is due to the laboriousness and complexity of obtaining a sufficient amount of data, processing and evaluating them, and presenting the results. At the same time, understanding the annual dynamics of collective sentiment provides us with important insights into collective behavior, especially in various crises or disasters. In our study, we propose a scheme for identifying and evaluating signs of the seasonal rise and fall of emotional tension based on social media texts. The analysis is based on Russian-language comments in VKontakte social network communities devoted to city news and the events of a small town in the Nizhny Novgorod region, Russia. Workflow steps include a statistical method for categorizing data, exploratory analysis to identify common patterns, data aggregation for modeling seasonal changes, the identification of typical data properties through clustering, and the formulation and validation of seasonality criteria. As a result of seasonality modeling, it is shown that the calendar seasonal model corresponds to the data, and the dynamics of emotional tension correlate with the seasons. The proposed methodology is useful for a wide range of social practice issues, such as monitoring public opinion or assessing irregular shifts in mass emotions.

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Interventions in Social Networks: Impact on Mood and Network Dynamics

Cited by 4 publications

References 34 publications

Exploring the Sources of Collective Effervescence: A Multilevel Study

Exploring the Sources of Collective Effervescence: A Multilevel Study

In the mood: the dynamics of collective sentiments on Twitter

Modeling Seasonality of Emotional Tension in Social Media

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