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Enregistrement W4284898246 · doi:10.1093/ijnp/pyac032.030

RESEARCH ON THE EXPRESSION OF PUBLIC EMOTION AND BEHAVIOR IN MICROBLOG PUBLIC OPINION

2022· article· en· W4284898246 sur OpenAlexaff
Jing Wei, Yuguang Jia, Henmin Zhu, Xiaojuan Hong, Weidong Huang, Marshall S. Jiang

Notice bibliographique

RevueThe International Journal of Neuropsychopharmacology · 2022
Typearticle
Langueen
DomainePhysics and Astronomy
ThématiqueOpinion Dynamics and Social Influence
Établissements canadiensBrock University
Organismes subventionnairesnon disponible
Mots-clésMicrobloggingSocial mediaPublic opinionAngerMoodPsychologyBeijingSocial psychologyPublic relationsPolitical scienceChinaLawPolitics

Résumé

récupéré en direct d'OpenAlex

Abstract Background Compared with the public opinion in the era of traditional media, microblog public opinion shows different characteristics, among which the strong expression of public emotion and behavior is important and has practical impact. The important factor that microblog public opinion has a significant influence is that microblog directly reflects the public mood. Unlike traditional mass media, microblog is a direct point-to-point communication, and the influence comes from the nested microblog relationship. This exponential communication influence enables the public mood to spread to every corner of the world in a short time. In an article entitled “Public opinion formed by anger”, the author quoted the research results of Beijing University of Aeronautics and Astronautics as saying that anger spreads faster on microblog than any emotion. The article also points out that microblogging has become the most convenient channel for Chinese people to participate in social discussion and express concern. The anger expressed in social contacts has promoted the dissemination of relevant news and accelerated the formation of public opinions and collective action. In fact, in many social fields in china, the trend of emotion in microblog has become an important aspect of public opinion prediction, even in crime prevention and economic development. Unfortunately, while people are keen to feel and use the emotional factors in microblog, they have not made a rational exploration of it. Based on the above background, this study intends to start from four aspects: First, why there are obvious emotional characteristics in microblog public opinion compared with traditional mass media; second, what kind of emotions exist in microblog public opinion and what is the relationship with the current social reality; third, how to express the emotions in microblog public opinion and how to present them; fourth, what are the social effects of emotional behavior in microblog public opinion and how to deal with the negative social effects. Subjects and Methods This paper takes psychological research as the main orientation, and pays attention to the public emotional factors in microblog public opinion. The main research methods are as follows: 1 literature method, through the collection and analysis of public opinion research, microblog research and public sentiment research, this paper selects the content related to this research and analyzes, arranges, synthesizes and uses it. Based on the above research, this paper puts forward a new dimension of public opinion research, and tries to innovate in research content, research perspective and research methods. 2. Case analysis method: On the basis of combing the context of the event, conduct text analysis and data statistics, register microblog through real name and interact with other users, and observe and collect relevant materials in the process of interaction. Questionnaire survey method in order to catch a glimpse of the leopard. 3. Questionnaire survey, using scl-90 questionnaire, 208 microblog creators were selected to investigate and analyze the emotional and behavioral factors. Ten factors include somatization, anxiety, compulsion and so on. Results The research found that with the continuous development of the internet, public emotional behavior will have an important impact on microblog public opinion. There are two directions: One is the top-down impact, the other is the bottom-up impact; it is mainly manifested in two typical ways: Social mobilization and emotional social struggle in microblog public opinion. The main expressions of their emotional behavior are: Weakness, anger, sadness and anger, etc. On the basis of combing the collective behavior and emotional struggle, the research finds that the communication framework of emotional behavior mainly includes the communication paths of discourse co meaning, identity co meaning and emotional co meaning; functional analysis includes target function, attribution function and ideographic function. From the tendency of public sentiment, microblog public opinion shows criticism, populism, nationalism, pragmatism, patriotism and justice. The social expression of public sentiment in microblog public opinion includes the spiral phenomenon of silence, butterfly effect, herd effect, resentment and so on. From the perspective of psychology, the public emotions in microblog public opinion are expressed as fear, anxiety, anger and sadness, while in terms of expression, the public express their feelings through direct expression, folk language and other ways. Conclusion The social effects of negative emotions in microblog public opinion have their own paths and standards of research and judgment, and we need to deal with the social effects of negative emotions. In the process of response, we should follow the principles of first time, multi opinion space construction, affinity and self-centered, strengthen the construction of service-oriented government, establish a three-dimensional communication pattern, and establish monitoring, early warning and feedback mechanisms. Acknowledgements This work was supported by the National Natural Science Foundation of China [grant numbers 71704085, 71874088] and Postgraduate Research & Practice Innovation Program of Jiangsu Province [Grant number KYCX21_0832].

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,702
Score d'incertitude au seuil0,523

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0010,000
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,092
Tête enseignante GPT0,414
Écart entre enseignants0,322 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations2
Publié2022
Routes d'admission1
Résumé présentoui

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Même revueThe International Journal of NeuropsychopharmacologyMême sujetOpinion Dynamics and Social InfluenceTravaux en français237 207