Identify Opinion Leaders Research Articles

Identifying Domain-specific Opinion Leaders in Twitter (X): An Optimized Approach

The role of opinion leaders on Twitter to analyze and understand the diffusion of information cannot be overlooked. The coming of the information age and the advent of social networking platforms have not eliminated opinion leadership but rather led to the emergence of its new forms. In this line, this paper deals with organically identifying opinion leaders on Twitter, based on the list feature. The method relies on the meta-data of Twitter lists, containing semantic cues to infer the topical expertise of its members. Based on the studies that have already shown the effectiveness of this method, this paper further illustrates in detail how the method can be employed flexibly to identify highly influential Twitter users in any specific domain. In this regard, the method can be adapted to different research questions, allowing researchers to apply it to suit their specific objectives and data. This paper also presents a novel approach as to how influential Twitter users identified through Twitter lists can be ranked. The ranking index proposed is attentive to both vertical (public perception and engagement) and horizontal (peer perception) dimensions of information diffusion.

Multiple criteria decision modeling in social networks: An application to identify opinion leaders in online public opinion events

For online public opinion events, the misconduct of opinion leaders can easily lead to the spread of negative public opinion, which in turn can adversely affect real society. In order to minimize this adverse impact to the greatest extent, this paper uses social network analysis to explore the attributes of individuals, features of network structure, and social relationships between decision makers (DMs). It constructs the structural system of evaluation criteria and a multiple criteria decision model to comprehensively and reasonably identify opinion leaders. It additionally considers the importance of criteria and the interaction among them. The hierarchical structure is combined with the evaluation level to construct a precise structural system of criteria. To handle the interaction, the Choquet integral is applied as the preference model. Based on the disaggregation principle, a generalized hierarchical level dependent Choquet integral is developed to reproduce additional preference information provided by decision analysts (DAs) and aggregate evaluation values of DMs on multiple interacting criteria. Then, the relevant indices of this integral and the preference information are applied to robust ordinal regression to mine rich preference relations among DMs. Furthermore, the hierarchical level dependent Choquet integral-driven multiple criteria decision model is proposed to rank DMs and identify opinion leaders. Finally, a numerical example and two real datasets validate the effectiveness and advantages of the proposed decision model.

X as a Passive Sensor to Identify Opinion Leaders: A Novel Method for Balancing Visibility and Community Engagement.

The identification of opinion leaders is a matter of great significance for companies and authorities, as these individuals are able to shape the opinions and attitudes of entire societies. In this paper, we consider X (formerly Twitter) as a passive sensor to identify opinion leaders. Given the unreliability of the traditional follower count metric due to the presence of fake accounts and farm bots, our approach combines the measures of visibility and community engagement to identify these influential individuals. Through an experimental evaluation involving approximately 4 million tweets, we showed two important findings: (i) relying solely on follower count or post frequency is inadequate for accurately identifying opinion leaders, (ii) opinion leaders are able to build community and gain visibility around specific themes. The results showed the benefits of using X as a passive sensor to identify opinion leaders, as the proposed method offers substantial advantages for those who are involved in social media communication strategies, including political campaigns, brand monitoring, and policymaking.

Twitter Opinion Leaders’ Detection (TOLD): A comprehensive approach

Opinion leaders are pivotal in shaping public opinion, influencing marketing strategies, guiding administrative decision-making and driving political discussions in online social networks. However, developing a standardized algorithm for detecting opinion leaders across diverse platforms is challenging due to the unique characteristics of different data sources. Each source has its structural properties, necessitating a tailored detection algorithm. In this paper, we propose a new hybrid approach, Twitter Opinion Leaders’ Detection (TOLD), designed for identifying opinion leaders on Twitter. TOLD combines a machine learning approach for computing user score in topical influence and domain influence, a statistical approach for measuring engagement and activity user score, a graph-based approach for measuring network structure influence user score and a PageRank-based approach for measuring popularity influence user score. We then identify opinion leaders through these multidimensional scores in each domain and across all domains. We compare TOLD with Topic-sensitive PageRank and TwitterRank methods, assessing their correlation and similarity. The results show that TOLD outperforms the other methods. Our approach offers a comprehensive and precise detection of opinion leaders related to particular domains, enriching our understanding of influential individuals in online communities.

SKILLSETS FOR FUTURE-READY OLDER WORKERS: SKILLS GAPS AND LEARNING BARRIERS IN SINGAPORE

Abstract Population ageing in Singapore is on a rapid rise as approximately 25% of Singaporeans will be aged 65 or over by 2030. The employment rate of older workers aged 55 and above rose from 24.3% in 2014 to 26.4% in 2021 (MOM , 2022). Older workers are at greater risk of skills mismatch. To enhance employability, governments, and international organizations explore appropriate responses to shifting skills, employability and training. This paper presents results of the qualitative phase in a two-year project that aimed to identify emerging skills, skills gaps, and training barriers among older persons in the Singaporean workforce and provide recommendations to policy makers to better support upskilling and reskilling among employees aged 55 and over. The project included 132 participants (29 age 45-54, 53 age < 55, 35 managers and 15 training providers) from three sectors: Retail, Food Services, and Precision Engineering. The qualitative data revealed skills mismatch resulting from the implementation of technological innovation in daily tasks. Learning barriers among older workers included accessibility to training, mindset and attitudes towards reskilling, fear of technology and training applicability. Recommendations to policy makers included practical solutions to enhance accessibility to course lists and career coaches; identify opinion leaders among older workers and company managers; closer collaboration between employers (inhouse training) and training providers for better follow-up and needs assessments; and utilize Andragogy theory for training design.

Social media opinion leaders and information diffusion of crowdfunding projects: Evidence from China

Opinion leaders play essential roles in online social media and are the key to analyzing the process of information diffusion. While the crowdfunding market has exhibited rapid growth, empirical studies about social media opinion leaders in the crowdfunding context remain nascent. Specifically, efforts to identify opinion leaders in social media and to understand the mechanisms by which such opinion leaders influence others in the crowdfunding context are limited. Based on the information diffusion theory, this study aims to address these research gaps by applying a multiple-case research design combined with dynamic network analysis (DNA). By analyzing user-generated content related to selected case projects from Weibo, the most popular micro-blogging site in China, the study identifies five types of social media opinion leaders. In addition, it investigates the various roles of opinion leaders in the information diffusion process on social media. Theoretically, this study broadens the boundaries of the “two-step flow” model and the diffusion of innovations theory by applying them to the crowdfunding context. The findings of this study provide valuable insights on crowdfunding project promotion for both crowdfunding platform operators and project initiators.

OPINION LEADERS ON THE INTERNET AND CONSUMER BEHAVIOR OF YOUNG PEOPLE

The main features of the influence of opinion leaders (influencers) on the Internet on the consumer behavior of young people are revealed. Theoretical grounds for studying such influence are presented. The leading concept is Paul Lazarsfeld's theory of limited effects of media exposure, including the hypothesis of a "two-stage flow" of information. Approaches to determining the criteria for identifying opinion leaders are considered. A number of classifications are presented. Macro-, micro- and nano-influencers stand out, as well as influencers-experts and influencers-charismatics. The main interests of young people related to accessing content created by bloggers on the Internet have been identified. Entertaining, leisure motivation prevails, but there is also a cognitive, informational aspect. Advertising of goods and services carried out by bloggers has some peculiarities. A significant place is occupied by native advertising. In general, the attitude towards advertising integration of bloggers towards young people is skeptical, but not nihilistic. Young people note that advertising is necessary to support the activities of bloggers. For many of them, this is an occasion to try something new, especially in the field of beauty and self-care. Advertising of bloggers is more credible due to the “similarity of interests”, according to which young people select opinion leaders for themselves.

OPINION LEADERS ON THE INTERNET AND CONSUMER BEHAVIOR OF YOUNG PEOPLE

The main features of the influence of opinion leaders (influencers) on the Internet on the consumer behavior of young people are revealed. Theoretical grounds for studying such influence are presented. The leading concept is Paul Lazarsfeld's theory of limited effects of media exposure, including the hypothesis of a "two-stage flow" of information. Approaches to determining the criteria for identifying opinion leaders are considered. A number of classifications are presented. Macro-, micro- and nano-influencers stand out, as well as influencers-experts and influencers-charismatics. The main interests of young people related to accessing content created by bloggers on the Internet have been identified. Entertaining, leisure motivation prevails, but there is also a cognitive, informational aspect. Advertising of goods and services carried out by bloggers has some peculiarities. A significant place is occupied by native advertising. In general, the attitude towards advertising integration of bloggers towards young people is skeptical, but not nihilistic. Young people note that advertising is necessary to support the activities of bloggers. For many of them, this is an occasion to try something new, especially in the field of beauty and self-care. Advertising of bloggers is more credible due to the “similarity of interests”, according to which young people select opinion leaders for themselves.

Recognition of opinion leaders in blockchain-based social networks by structural information and content contribution.

Exploring the influence of social network users in the blockchain environment and identifying opinion leaders can help understand the information dissemination characteristics of blockchain social networks, direct the discovery of quality content, and avoid the spread of rumors. Members of blockchain-based social networks are given new responsibilities by token awards and consensus voting, which alters how users connect to the network and engage with one another. Based on blockchain theory and the relevant theories of opinion leaders in social networks, this article combines structural information and content contributions to identify opinion leaders. Firstly, user influence indicators are defined from the perspective of network structure and behavioral characteristics of user contributions. Then, ECWM is constructed, which combines the entropy weight method and the criteria importance through intercriteria correlation (CRITIC) weighting method to address the correlation and diversity among indicators. Furthermore, an improved Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), called ECWM-TOPSIS, is proposed to identify opinion leaders in blockchain social networks. Moreover, to verify the effectiveness of the method, we conducted a comparative analysis of the proposed algorithm on the blockchain social platform Steemit by using two different methods (voting score and forwarding rate). The results show that ECWM-TOPSIS produces significantly higher performance than other methods for all selected top N opinion leaders.

The spatial difference of multi-layer climate change information flow and network construction: A comparison of “dual carbon” scenarios

Climate change is a critical topic in environmental communication. Through effective information communication, efforts and contributions to reducing greenhouse gas emissions can gain more social support, boosting carbon neutrality. In this study, the spatial distribution of the multi-layer information flow of climate change, the construction of forwarding networks, and the distribution of opinion leaders were studied by a web crawler and social network analysis. The comparison of the two periods reveals that: (1) The information diffusion depth of climate change information reaches up to three layers. Blog posts in relation to climate change are more likely to be shared in economically developed regions. (2) There is little direct correlation between the nodes in the climate change forwarding network. The communication network's structural integrity is poor, and the network structure is loose. (3) Following the proposal of the goal of “dual carbon”, more groups emerge to participate in climate change discussions. The media, celebrities, and government users take the leading role in climate change communication. The research findings are extremely important in clarifying the spatial pattern of climate change communication among provinces and identifying opinion leaders.

Identifying opinion leaders and narrative agency in the rejection omnibus law discourse

As a social media platform, Twitter can become an arena for civil citizens to contest the state discourse. The rejection of the Omnibus Law Bill proves the legitimacy of the massive resistance and mass mobilisation to reject policy products that contradict the public interest. This research was meant to provide a social media analysis of the issues of the plan of legitimation of the Omnibus Law Bill by employing two analysis methods: social network analysis and descriptive qualitative method. Twitter interactions during the Omnibus Law Bill rejection era were dominated by civil actors. The primary activity on Twitter was not only civil movements but also confronting individuals who could help to form public ideas. Within the agency’s narrative framework, Twitter interactions related to Omnibus Law Bill are communal and social endeavours as part of participation in digital activism. Other dictions were also visible within various hashtags constructed to assert the agent’s political position. Various content and digital media platform were produced as the media for transferring information and knowledge. They were put for the best use in a beautiful package. By opinion leaders’ agency, the narrative built by the State can be contested to influence others.

Male and stale? Questioning the role of “opinion leaders” in agricultural programs

Social networks can influence people’s behaviour and therefore it is assumed that central individuals in social networks, also called “opinion leaders”, play a key role in driving change in agricultural and food systems. I analyse the outcomes of an intervention (that encouraged Sulawesi smallholder farmers to take a specific action toward improving the health of their cocoa trees) to assess the impact of engaging opinion leaders in agricultural programs that aim to change farmers’ practices. The intervention has been implemented through (a) 18 opinion leaders identified by interviews and a social network survey of 1885 cocoa farmers; and (b) 18 randomly selected farmers who were not central in local social networks. The obtained social networks and statistical data were quantitatively analysed and the results were interpreted with input from the field staff. Contrary to expectations, the highly socially central opinion leaders were not more effective in promoting the initiative in their communities. On average, randomly selected low-centrality farmers convinced almost twice as many of their peers to take the recommended action as compared to the identified opinion leaders (17.1 versus 8.6) but the variation within the random group was also significantly higher. Importantly, while the identified opinion leaders were mostly senior men, women performed better in influencing others into taking action even when their centrality in local social networks of agricultural advice was lower. I discuss the implications of the conventional selection of perceived opinion leaders as model farmers for achieving sustainable and equitable change at scale in agriculture and propose practical alternatives.

Self-Sampling Methods for STI Screening among College Women: A Qualitative Study to Identify Opinion Leaders

Background Nearly half of new sexually transmitted infections (STIs) occur in young adults, however, less than half of young adult women receive STI screening annually. Novel screening strategies could aid in reducing screening barriers and improving screening rates. Self-sampling methods (sample collection at home) could be an innovative solution to improve screening rates among young adult women: however, there is little research exploring how knowledge of self-sampling methods is shared with this population. Thus, the purpose of this study was to investigate the influence of opinion leaders on self-sampling methods for STI screening among college women. Methods Participants (n=24) were sexually active, young adult (18-24), college women who attended a university in the southeastern United States. The interview guide was based on the opinion leaders construct from the Diffusion of Innovation theory. Two coders independently coded 10% of interviews with a Cohen’s Kappa of .84, and subsequent interviews were thematically coded by a singular coder. Results Opinion leaders fell into two categories: internal and external, with internal including resident assistants, student government leaders, coaches and captains, and sorority leaders. These campus leaders were discussed as opinion leaders because they influence decision-making and are seen as role models and trustworthy. External opinion leaders included celebrities or artists who were open about health issues or sexual topics and politically involved celebrities, who may have larger platforms to discuss self-sampling methods. Discussion New, innovative strategies for STI screening, such as self-sampling methods, could be a solution to overcome the barriers hindering annual STI screening among college women. Opinion leaders may play an important role in shaping the perception and dissemination of knowledge regarding self-sampling methods for STI screening among this demographic. Conclusion Opinion leaders can be used as conduits for health information on self-sampling methods within the college community and in the social system at a broader level.

Exploring online opinion leadership in the network paradigm: An analysis of influential users on Twitter shaping conversations around anthem protests by prominent athletes

The current study seeks to extend the network paradigm in public relations research by exploring the role of online opinion leaders in Twitter conversations around anthem protests by prominent athletes. The aim of the study is twofold: (1) identify opinion leaders involved in Twitter conversations related to anthem protests by Colin Kaepernick and Megan Rapinoe, and (2) further understand how and why social media users participate in conversations online about controversial subjects. The study combines social network analysis with in-depth interviews to adopt a more holistic framework for studying online opinion leadership in the context of public relations research. Ultimately, results from this study extend the network paradigm in public relations by examining the role of individual users in the construction of the discursive landscape around issue networks. Additionally, findings suggest that online opinion leaders should be differentiated from social media influencers in public relations scholarship as they reflect the movement away from Homo Economicus toward Homo Dialogicus (Kent & Taylor, 2016) and their capacity to facilitate the formation of publics and counterpublics around particular issues.

Learning to rank complex network node based on the self-supervised graph convolution model

Node ranking in complex networks is of great value in many fields, such as identifying opinion leaders in social networks and risky institutions in financial networks. However, traditional ranking methods are built on heuristic rules and may be effective in certain datasets and fail in others. To address the issues, we formulate node ranking on complex networks as a learning to rank problem, and propose a novel model based on self-supervised learning and graph convolution model to rank nodes based on integrated information from node features and network structure. (1) We develop a self-supervised pretext task to extract information about node location and global topology from complex networks. (2) To train the model more efficiently, multi-task learning is adopted, which includes ranking task and regression task besides the self-supervised pretext task. Our model works well with only a small number of nodes that have ranking labels. Comprehensive experiments on different datasets demonstrate that it outperforms the existing state-of-the-art methods, which verify the effectiveness and robustness of the proposed approach.

The Political Landscape of the U.S. Twitterverse

ABSTRACT Prior research suggests that Twitter users in the United States are more politically engaged and more partisan than the American citizenry, who are generally characterized by low levels of political knowledge and disinterest in political affairs. This study seeks to understand this disconnect by conducting an observational analysis of the most popular accounts on American Twitter. We identify opinion leaders by drawing random samples of ordinary American Twitter users and observing whom they follow. We estimate the ideological leaning and political relevance of these opinion leaders and crowdsource estimates of perceived ideology. We find little evidence that American Twitter is as politicized as it is made out to be, with politics and hard news outlets constituting a small subset of these opinion leaders. Ordinary Americans are significantly more likely to follow nonpolitical opinion leaders on Twitter than political opinion leaders. We find no evidence of polarization among these opinion leaders either. While a few political professional categories are more polarized than others, the overall polarization dissipates when we factor in the rate at which the opinion leaders tweet: a large number of vocal nonpartisan opinion leaders drowns out the partisan voices on the platform. Our results suggest that the degree to which Twitter is political has likely been overstated in the past. Our findings have implications about how we use Twitter and social media, in general, to represent public opinion in the United States.

Network versus content: The effectiveness in identifying opinion leaders in an online social network with empirical evaluation

Network studies predict individuals with prominent positions in a social network to be more influential. However, such influence is mostly evaluated by propagation assumption that an individual disseminates information to others, while whether such information has impact on the receivers is not examined. This paper focuses on a detailed scenario of Yelp, an online review platform where users are voted as helpful or not by others. As such, the empirical number of votes can be an alternative ground truth for user influence, to complement the simulation-based propagation ability. We explore whether the network features or the content features of the users are more determinative for identifying opinion leaders. Results suggest that the network features can better predict users’ propagation influence, but fail to predict the empirical collective votes. The content features, on the other hand, though not able to explain the propagation influence, are better indicators for the voted opinion leaders. Via a generative model, we argue two possible mechanisms of users accumulating influence, namely the network contagion which can be well predicted by the network features, and the natural accretion which is determined by the quality of contents created by users. In most real-world systems, both mechanisms may take effect. Our study highlights the necessity of distinguishing such different mechanisms, and selecting appropriate network and content features for prediction accordingly.

From past to present: Spam detection and identifying opinion leaders in social networks

From past to present: Spam detection and identifying opinion leaders in social networks

Using Social Network Analysis to Identify Opinion Leaders in Agricultural Knowledge Exchange and Decision-Making Network of Farm Women

The use of social network theories dates back to 1930s with the pioneering works of psycho-sociologist Moreno. With the advent of advanced technologies, there has been an explosion of its applications to various disciplines. The study applies social network theory to analyze agricultural knowledge exchange and decision-making network of farm women. The analysis identifies farm women who occupy central and strategic positions and acts as opinion leaders in the network. The study was carried out in the Himalayan regions of Uttarakhand, India. Descriptive research design and multi-stage sampling were adopted. A total of 298 respondents were selected purposively from the two villages of Uttarakhand. Data were collected through survey sociometric method. UCINET and SPSS were used for data analysis and interpretations. The findings showed that high in degree and Out degree centrality was recorded by 24 (12, 12) and 48 (26, 22) farm women in the two study villages. Total 24 opinion leaders were identified within the agricultural knowledge exchange and decision-making network. Their extent of opinion leadership was influenced by age, marital status, farming experience, socio-economic status, innovativeness, achievement motivation, decision making ability, risk preference, economic motivation, information seeking behaviour, cosmopoliteness and social participation.

Politicised opinion leaders in the younger generation: to meat or not to meat?

PurposeThe consumption of animal-based food products faces several sustainability challenges. To date, however, meat intake plays an important role in everyday food choices. With their ability to change the opinions of a critical mass, opinion leaders in food choices are assumed to play a predominant role in influencing future dietary styles. Thus, the objective of this study was to identify opinion leaders in food choices and their personal meat consumption behaviour as well as their attitude towards policy interventions aiming to meat reduction.Design/methodology/approachThe sample consisted of 1,479 German participants aged between 15 and 29 years who were online surveyed in autumn 2020. A latent profile analysis (LPA) identified three distinct groups of opinion leader in the younger generation labelled “non-opinion leaders”, “weak opinion leaders” and “opinion leaders”. The identified profiles were used to understand opinion leaders and their food choices by using chi-square tests as well as univariate ANOVA with Tukey or Games-Howell post hoc tests.FindingsOpinion leadership in food choices was associated with a higher interest in meat-reduced dietary styles and with more positive attitudes towards innovative food ideas. Moreover, opinion leaders were associated with politicised food decisions, indicating that their food choices align with their political and social interests.Originality/valueThe results contribute to a better understanding of the development of future dietary styles, provide evidence for a shift towards more sustainable dietary patterns in the near future and highlight that food decisions are no longer solely decisions on an individual basis but rather becoming of political relevance.