Social Opinion Research Articles

What Makes a Social Media User an Opinion Leader? Source Characteristics and Consumers’ Behavioral Intentions

Instagram as a social media platform has gained prominence as the medium of influencer marketing practices targeting consumers of beauty products, yet little is known about source characteristics affecting Instagram opinion leadership resulting in consumers’ purchasing behavior. Thus, this work examines effects of Instagram influencers’ attributes (credibility, attractiveness, innovativeness, and sense of humor) on their perceived opinion leadership and its subsequent impacts on consumers’ behavioral intentions. The research also analyzes complementary moderating effects of parasocial relationship and social identity reinforcing behavioral implications of social media opinion leadership. Empirical results from 202 Instagram followers reveal that credibility, innovativeness, and sense of humor exert positive influence on perceived opinion leadership which subsequently enhances levels of positive word-of-mouth, purchase, and loyalty intentions. Besides, social identity strengthens positive impacts of opinion leadership on consumers’ word-of-mouth and purchase intentions. Thus, the findings not only contribute to the theory of opinion leadership, but have beneficial implications enabling beauty brands to develop effectual social media marketing communication strategies.

Unveiling the Influences: Self-Esteem and Peer Impact on Luxury Consumption Among Consumers

In order to examine the impacts of peer influence and self-esteem on luxury consumption. This research employed online and offline questionnaire to gather customer self-esteem degrees, personality characteristics, along with conspicuous consumption behaviors. The information and data were construed through one-way analysis of variance (ANOVA) and multiple regression analysis. Accordingly, it was indicated that customers with higher degrees of self-esteem showed firmer willingness and action in luxury purchase intentions, and peer influence remarkably impacted their conspicuous consumption. In brief, self-esteem and peer influence are both key players in luxury consumption.Researches have revealed that consumers buy articles of luxury, in an attempt to satisfy the demands for materials and improve their self-worth and social positions. Luxury consumption is prominently impacted by collective self-esteem, since consumers believe that when they purchase luxury goods, their social position and personal image will improve. Moreover, peer correlations exert a highly important effect on young customers, which is deeply intensified by social media and celebrity effects.According to the findings, luxury brands need to take into consideration these points with regard to developing marketing tactics: for starters, meeting the demands of customers with discrepant degrees of self-esteem through individualized marketing tactics, with a focus on the product uniqueness and brand recognition. Secondly, through the transmission of brand stories and values, enhance emotional connections among consumers. Finally, leverage the influence of social media and opinion leaders to attract and retain young consumers. Through these strategies, luxury brands can better meet consumer needs and enhance market competitiveness.

Understanding public opinion formation in Southeast Europe: Exploring social forces and communication dynamics

The article investigates the mechanisms through which social forces, networks, cognitive frames, and institutions shape public opinion in Southeast Europe via communication. Employing a qualitative approach, data was collected through synchronous focus groups conducted in six Southeast European countries. The findings reveal that social networks play a pivotal role in shaping public opinion, with organizational position and relationships influencing communication effectiveness. Cognitive frames significantly impact public perception, with entrenched beliefs guiding interpretation of information. Meanwhile, social institutions, such as language and communication rules, influence the clarity and accessibility of messages, thereby shaping public understanding. The research underscores the need for deeper research, ethical standards, and transparent communication to build and maintain public trust. Continuous engagement with the public and adaptability in communication strategies are essential for fostering mutual understanding and trust. This research emphasizes social forces’ complexity and overlapping nature in shaping public opinion and the strategic approaches required for effective communication in Southeast Europe.

Cognitive dissonance and introversion effects on opinion dynamics and echo chamber formation

Bounded confidence models (BCM) are extensively used to model continuous opinion dynamics in social networks. Typically, these models are analysed on static networks where edges do not vary over time. Following in the footsteps of adaptive voter models, further research has considered BCMs in the setting where agents are able to dynamically adjust their edges, which subsequently feedback into the opinion dynamics of the network. Several methods of updating connections have been proposed ranging from random rewiring to more sophisticated approaches based on concordant edges, homophily and cognitive dissonance. We present a modified form of the bounded confidence model, termed the selfish agent opinion (SAO) model, where connection updates are evaluated using a general cost function. Agents in the SAO model maintain two classes of relationships, friends and acquaintances, based on which they update their opinions and edges to optimise a payoff function that may include multiple social factors. This paper explores the effects, which we describe as “cognitive dissonance” and “introversion” that attempt to mimic postulated behaviours associated with the social concepts of the same name, on social network and opinion dynamics. We find that the SAO model naturally produces echo chambers for social networks with increased sensitivity to cognitive dissonance, whilst introversion produces high levels of fragmentation and low opinion mobility. Additionally, the effect of tolerant agents and inquisitive social encounters is investigated. It is found that both the presence of very small numbers of tolerant agents and inquisitive encounters are able to strongly promote consensus formation.

The reputation capital of an individual in business environment and its impact on company’s reputation

In the modern world, the desire to be well perceived by the environment has become a duty, which is the only way to achieve a competitive advantage and success. Today, the reputation capital of an individual is of great importance, especially on the labor market. Everyone wants to be an important part of the team. To have respect and love in the workplace, it is necessary to develop and increase your reputation capital. In this article, the authors consider in detail the concept of «reputation», including the approaches of domestic and foreign authors, study the concept of «business reputation», its main characteristics, elements and advantages. The paper studies the ways to increase the reputation capital of an individual in business environment, as well as mistakes that should be avoided. It notes the actions that should be taken if someone wants to blacken business reputation for their own benefit. This article highlights and describes the impact of a person's reputation capital on the reputation of an enterprise based on the results of a Weber Shandwick survey, and also examines the company's impact on the reputation of employees: investigates key criteria of a future employer for most applicants, analyzes certain methods and tools for retaining valuable employees in the workplace in the long term. The article concludes with is formulating the concept of «reputation» as a social opinion, justified or not, about a certain object. A good reputation is synonymous with prestige, fame and a good name; a bad reputation is equated with dishonor or discredit.

Social Network Public Opinion Analysis Using BERT-BMA in Big Data Environment

The existing social network public opinion analysis methods have problems such as poor semantic expression quality and weak detection ability in short texts. Therefore, a social network public opinion analysis method based on BERT-BMA is proposed. To normalize the comment text, the rumor text is initially transferred to a word vector matrix using the BERT (Bidirectional Encoder Representations from Transformer) model. The BiLSTM-based network architecture is subsequently employed to acquire the trace features of data transmission. Ultimately, this study employs the multi-head attention mechanism to extract feature information that is more significant in the analysis of online public opinion by mining the dependency relationships between users, resulting in increasing ability to detect public opinion emergencies. The experimental outcomes indicate that the results on the Twitter data set and Weibo dataset are superior to other comparative models.

The Association Between Mental Health and Different Sexual Orientations

In society, many bisexual or homosexual people suffer from depression and other mental diseases because of social factors such as social prejudice and social opinion. This work will focus on the relationship between mental health and sexual orientation, and it will also develop a detailed understanding of the factors that affect sexual orientation. By combining these factors, the paper concluded that bisexuals are more likely to suffer from depression, and heterosexuals are less likely to suffer from depression. This article also summarize some factors of mental health problems led by bisexuality, which are sexual orientation-based discrimination, bisexual invisibility and erasure, and lack of bisexual-affirmative support. In addition, this paper also involves a survey, which research about teenagers opinion toward sexual orientation, it emphasizes that people should always maintain an objective and tolerant attitude towards all kinds of people. People do not need to understand people with different sexual orientations, but they should respect them.

A Large-Scale Group Decision-Making Model Considering Expert Authority Degree and Relationship Evolution Under Social Network

In Large-Scale Group Decision-Making (LSGDM), effectively implementing consensus models is pivotal for managing decision complexity. While trust-based LSGDM has garnered attention, there remains a need for deeper insights into the dynamics of interexpert trust and the impact of authority effects on the decision-making process. This study introduces a sophisticated model for large-scale group decision-making, incorporating considerations of expert “trustworthiness-authority.” Initially, the study assesses the trustworthiness of experts based on social network relationships and opinion similarity while using background information and consensus levels to establish their authority. Subsequently, experts are categorized into four distinct regions based on their trustworthiness and authority assessments. Furthermore, tailored consensus adjustment methods are proposed for each region based on social contagion theory to facilitate consensus achievement. Additionally, a case study is conducted to demonstrate the rationality and effectiveness of the proposed LSGDM model, considering expert “trustworthiness-authority.” Finally, the necessity and superiority of the proposed model are further verified through comparison analysis and sensitivity analysis.

Text sentiment analysis and classification based on bidirectional long and short term memory networks

In this study, we explored the method of text sentiment analysis and classification using bi-directional long and short-term memory networks, which brings new ideas to the field of sentiment classification. In the text pre-processing stage, we processed chat phrases, non-alphanumeric characters, deactivated words, numbers, special characters, blanks, and addresses to convert the raw text data into numerical data that can be processed by the model. Subsequently, word frequency statistics were conducted for texts of different mood types to analyse the frequency of occurrence of different words in texts of different mood types. By introducing a bi-directional long and short-term memory network model for training, we observed the accuracy and loss value performance on the training and validation sets. The accuracy of the training set increases from the initial 94.6% to 99.5%, and the accuracy of the validation set gradually grows to 99.4%; at the same time, the loss value of the training set decreases from the initial value of 0.2075 to 0.0194, and the loss value of the validation set gradually increases from the initial value of 0.0538 to 0.0372. This indicates that the bi-directional long short-term memory network achieves a significant and stable performance improvement. The confusion matrix results for the test set show that our model achieves more than 99% prediction accuracy on the test set. This implies that our proposed method can efficiently classify textual sentiment with high prediction accuracy. This research result helps to improve the effectiveness of the application of sentiment analysis techniques in the fields of social media opinion monitoring and product review analysis, and provides a useful reference for further deepening the research on text sentiment analysis. In conclusion, this study achieves the optimisation of the text sentiment classification task by means of a bidirectional long and short-term memory network model, and achieves satisfactory results. In the future, the model performance and generalisation ability can be further improved by combining more corpus and optimising the model structure, so as to be better applied in real-world scenarios.

Islamic and sectarian or secular and nationalist? New classification of Iraqi Shi’a political actors based on ideological anchorage

ABSTRACT Since the fall of the Saddam Hussein regime in 2003, Iraqi Shi’a political actors have been the main forces leading the country, dominating the domestic legislative scene over their Sunni and Kurdish counterparts. This article brings innovative analysis and classification of actors, such as Islamic Da’wa Party, Badr Organization, Asa’ib Ahl al-Haq (AAH), Harakat Huquq, Al-Hikma, Islamic Supreme Council of Iraq (ISCI), Sadrist Movement, or Imtidad, bringing more light on the Iraqi political scene since the parliamentary elections in 2021. The ideological anchorage of the parties is evaluated in two dimensions, religious and sectarian, placing them on the Islamic-secular and sectarian-nationalist axes, reflecting their approach towards intra-religious issues, vilayet al-faqih, possible normalization of relations with Israel, sectarianism, and nationalism. The methodology of this research is based on Chapel Hill Expert Survey data collection, semi-structured interviews with politicians and political experts from the Kurdistan Region of Iraq and Iraq, Jordan, and the United Kingdom, content analysis of examined actors’ statements on social and official media and opinion polls, reflecting the voices of the Iraqi society. The main results of this research singled out clusters of more pro-Iranian actors (Badr Organization, AAH, Harakat Huquq), with the highest tendency to use Islamic and sectarian policies, followed by more traditional Iraqi political actors such as Islamic Da’wa Party and ISCI. Al-Hikma, Sadrist Movement and Imtidad then inclined to be more nationalist and secular. Finally, it shows the Iranian influence as a crucial factor in shaping the domestic and foreign policies of thoseactors.

Swayed by leading questions

AbstractThe use of leading questions as part of interrogation techniques and the factors associated with the witness suggestibility are well researched in the scientific literature. Nevertheless, literature on suggestibility in intentionally or unintentionally poorly designed social opinion surveys and push polls is scarce. The objective of this paper was to test how easily the public is swayed by leading questions in poorly designed surveys, whether certain group types of respondents in terms of their susceptibility exist, and to investigate the relationship between several demographic and personality characteristics and susceptibility. For this purpose, an online self-administered survey was conducted, containing a block of neutral and several blocks of leading questions about the respondents’ opinions on minority groups. The results indicated that the negative wording in questions was reflected in a more negative opinion on minority groups, while there are differences between respondents in their level of suggestibility. Three groups of respondents exist, i.e., non-suggestible (with no shift in opinion), suggestible (who express a more negative opinion on minorities following the suggestion), and contra-suggestible respondents (who react on the contrary and give a more positive opinion after a negative suggestion). Susceptibility to suggestion is associated with respondents’ age, openness to experience, conscientiousness, and social desirability. The results suggest that contra-suggestibility might counterbalance the error made by the leading questions, at least when more controversial topics are being surveyed.

Application of Deep Learning in Social Network Public Opinion Sentiment

With the rapid development of social network platform, in order to purify the network environment, prevent the abuse of public opinion information, and control public opinion in a very short time, this paper proposes the application research of deep learning in social network public opinion sentiment recognition and analysis. Through the construction of social network model and social network knowledge map and the analysis of key technology, a network public opinion algorithm based on deep learning is proposed, and a competitive public opinion information communication model in online social networks is constructed, and then a simulation experiment is conducted on the improved model of the constructed social networks. The result shows that in public opinion management and control, for users with larger node degree, this technology can effectively understand and obtain public opinion information in a very short time with faster and wider information propagation speed, so as to realize the effective control of public opinion information and the processing of massive information.

Extracting Retrospective Information through the Public Sharing of Female Golfers' Contributions in a Digital Environment: An Analytical Perspective of Their Impact on Shaping Golf Communities

Abstract Social media are an integrated part of everyday life. They are creating a new paradigm of information consumption. The era of popularity of searching for information in large texts is irreversibly gone. The current model of communication and information distribution favours visual content. One of the major social media platforms that specializes in expressive visual content is Instagram. The essence of a communication idea is effectively rendered in a unique image. Thus, it becomes a key tool for the transmission of communication. In the above technical paper, we present empirical data that investigate the impact of Instagram posts of three professional women golfers during the calendar year 2022. The first one is the most followed golfer on social networks, the second one ranks among the current best professional golfers and the third one holds the position of the president of the Slovak Ladies Golf Association. These prominent figures in golf create a significant impact on the social opinion of the sport. The central aim of the present study is to comprehensively understand how certain types of visual content, also created through AI, elicit maximum audience engagement on social media. We present the results in the context of correlating the influence of different factors on the level of viewer interaction. The value is expressed through the number of likes awarded and the size of the membership base of the affected accounts.

Universality of opinions disappearing in sociophysical models of opinion dynamics: From initial multitude of opinions to ultimate consensus.

Possibility of reaching a consensus in social systems with strong initial fragmentation is one of the most interesting issues in sociophysics. It is also intriguing what the dynamics of such processes is. To address those problems, we performed computer simulations using well-established models of social opinion formation, namely, the voter, Sznajd, and Latané models. We investigated opinion dynamics in cases where the initial number of opinions is very large, equal to the number of actors (the voter and Latané models) or when every second actor has their own opinion (Sznajd model), with some variations on the update schemes, lattice topologies, effective ranges of interaction, and information noise levels. For all considered models, the number of opinions assumed by the actors is finally almost always reduced to only one. However, while the voter and Latané models exhibit a power-law time decrease in the number of opinions, the Sznajd model follows a complex three-stage behavior. We also demonstrated the mean/median time of reaching the consensus scales with system size according to a power law for voter and Sznajd models, while for the Latané model, this increase is even faster. Our results show that in the studied models, the consensus is possible, provided that a long enough and model-dependent time to reach this state is available.

An Analytical Study on the Logic of Tourism Consumption Policies under the Perspective of Multi-Stream Theory in the Post-Epidemic Era---Taking Harbin City as an Example

In recent years, the tourism industry was one of the first sectors to be seriously affected by the New Crown epidemic. Therefore, in the post-epidemic era, China has introduced a series of policies to promote tourism consumption. Based on a multiple streams perspective, this paper explores the internal logic behind the formulation of tourism consumption policies by taking Harbin City as an example, which involves issues, policies, and political perspectives. The study finds that there are problems, policies, and politics behind the formulation of a policy and the setting of an agenda at the social level. Problem streams such as the single structure of tourism consumption and poor service quality are the main sources of policy change; the research results of experts and scholars are the policy stream of policy change; and social opinion and the direction of national sentiment are the political stream of policy change. The three streams converge at a certain point in time and open the window of policy. To further promote the development and improvement of the tourism consumption market, Harbin City should establish a sense of the problem and adhere to the problem orientation in the future; widely solicit suggestions and adhere to the policy formulation from multiple angles; and uphold the concept of service and create a good public opinion environment.

Social forecasting: Online social opinion and the cross-section of stock returns

We construct a monthly measure for online social opinions on stocks in the Chinese market by extracting textual data from the internet. By implementing a “social forecasting” strategy, we find a significantly positive alpha of 0.544% per month based on the model of Fama-French's (2015) five-factor plus Carhart's (1997) momentum, and 0.636% per month based on Liu et al.'s (2018) Chinese four-factor model. This anomaly is driven by the mispricing of underexplored information since online social opinions are a strong predictor of firms' earning surprise and are strengthened when there are information frictions, such as low investor attention, high arbitrage costs, high proportions of sentiment-driven trades, and low percentages of institutional investors. Our results are robust to a series of tests and remain unchanged after taking the “factor zoo” into consideration.

Fake User Detection Based on Multi-Model Joint Representation

The existing deep learning-based detection of fake information focuses on the transient detection of news itself. Compared to user category profile mining and detection, transient detection is prone to higher misjudgment rates due to the limitations of insufficient temporal information, posing new challenges to social public opinion monitoring tasks such as fake user detection. This paper proposes a multimodal aggregation portrait model (MAPM) based on multi-model joint representation for social media platforms. It constructs a deep learning-based multimodal fake user detection framework by analyzing user behavior datasets within a time retrospective window. It integrates a pre-trained Domain Large Model to represent user behavior data across multiple modalities, thereby constructing a high-generalization implicit behavior feature spectrum for users. In response to the tendency of existing fake user behavior mining to neglect time-series features, this study introduces an improved network called Sequence Interval Detection Net (SIDN) based on Sequence to Sequence (seq2seq) to characterize time interval sequence behaviors, achieving strong expressive capabilities for detecting fake behaviors within the time window. Ultimately, the amalgamation of latent behavioral features and explicit characteristics serves as the input for spectral clustering in detecting fraudulent users. The experimental results on Weibo real dataset demonstrate that the proposed model outperforms the detection utilizing explicit user features, with an improvement of 27.0% in detection accuracy.

Information processing of travel barrage reviews: effects of social presence and opinion leaders on the relationship between barrage reviews and travel intention

ABSTRACT Barrage as an emerging type of user-generated review has been adopted by almost all tourism live-streaming platforms. However, few studies have been conducted to understand how travel barrage review attributes affect travel intention (TI). This study adopts a mixed method to examine the effectiveness of the content and form attributes of travel barrage reviews in influencing potential tourists’ travel intentions. Two studies jointly revealed that potential tourists were more willing to adopt the central path to process barrage content, while perceived trust and usefulness played mediating roles. Conversely, potential tourists were more willing to adopt the peripheral path to process barrage form when social presence played a mediating role. Furthermore, the moderating effect of opinion leaders on the relationship between barrage reviews and changing TI was considered. This study extended previous work in the online travel reviews field and provided guidance to tourism practitioners on how to engage in live-streaming marketing.

Social network group decision-making model considering interactions between trust relationships and opinion evolution

PurposeSocial network group decision-making (SNGDM) has rapidly developed because of the impact of social relationships on decision-making behavior. However, not only do social relationships affect decision-making behavior, but decision-making behavior also affects social relationships. Such complicated interactions are rarely considered in current research. To bridge this gap, this study proposes an SNGDM model that considers the interaction between social trust relationships and opinion evolution.Design/methodology/approachFirst, the trust propagation and aggregation operators are improved to obtain a complete social trust relationship among decision-makers (DMs). Second, the evolution of preference information under the influence of trust relationships is measured, and the development of trust relationships during consensus interactions is predicted. Finally, the iteration of consensus interactions is simulated using an opinion dynamics model. A case study is used to verify the feasibility of the proposed model.FindingsThe proposed model can predict consensus achievement based on a group’s initial trust relationship and preference information and effectively captures the dynamic characteristics of opinion evolution in social networks.Originality/valueThis study proposes an SNGDM model that considers the interaction of trust and opinion. The proposed model improves trust propagation and aggregation operators, determines improved preference information based on the existing trust relationships and predicts the evolution of trust relationships in the consensus process. The dynamic interaction between the two accelerates DMs to reach a consensus.

Mechanism of Formation and Propagation of Internet Rumors from the Perspective of Social Psychology

Rumor is a typical social public opinion phenomenon. With the advent of the internet era, the intensity and breadth of internet rumors have increased, resulting in more profound and prominent impacts on society. Current research on internet rumors mainly involves theoretical analysis, but lacks a clear, common theoretical perspective. The social psychology communication perspective is precisely the perspective that studies how social information is disseminated, how individuals and groups in society perceive information dissemination, and how individuals and groups in society respond to information dissemination. This study argues that the social psychology communication perspective is an important theoretical perspective for analyzing the phenomenon of internet rumors. This study suggests that mechanisms such as diffusion of responsibility, cognitive closure, thinking patterns, self-serving bias, and emotional contagion contribute to the continuous generation and rapid spread of internet rumors. This paper also proposes countermeasures and suggestions for governing internet rumors from the perspectives of multiple social entities such as media and regulatory agencies.