Opinion Leadership

New technologies allow politicians to spread their messages omitting the role of mediators. In this context, the Internet has also promoted the emergence of a new actor, digital opinion leaders, who go beyond traditional politics and seek to set the public agenda. One of the main questions nowadays is whether social media, and in particular Twitter as a consolidated tool for political communication, is only used as a sounding board for their political statements, spurring the messages of populist forces. With this in mind, the main objective of this research is to explore the influence of the far-right in the public debate of political leaders on Twitter, analyzing the specific case of the Andalusian regional elections held in December 2018. These elections can be considered a political turning point, with an extreme right party winning seats in a Spanish regional election for the first time in 35 years. In this paper we analyze if Vox used a differentiated strategy via this social network compared to the candidates of the traditional parties: PSOE, PP, Ciudadanos, and Adelante Andalucía. Using content analysis on Twitter as a method, this research determines how Vox candidates worked as influencers of the digital political debate, despite being extra-parliamentary. Vox marked the agenda for the rest of the leaders, while generating great expectation among the audience.

As the number of microblogging tools and social media users rapidly increases, it is important to understand who and what is being disseminated. One emerging area of research is the use of Twitter as a means for the masses to voice dissent and mobilize action. This study analyzed the features of the top 30 tweeters and top 30 retweeted users during the 2011 protests in Egypt. By conducting a content analysis of tweets from the #jan25 data set (N = 600), we found that the characteristics of the source including location, media affiliation, longer account duration, and number of followers positively impacted retweetability. Power users or those tweeters sending the most messages were more likely to be based in Egypt than in other areas. Users who retweet messages seem to constitute a different group with more passive characteristics. The content of messages such as language intensity, information or URL sharing, and call to action were not significant predictors of retweetability. Because tweets had the same underlying theme, that is, dissent against the prevailing government, characteristics of the source differentiated the top tweeters from the top retweeted users.

Opinion leaders are important sources of advice for other consumers. Instagram is the most used platform by opinion leaders in the fashion industry, and this trend is expected to continue in the near future. This study aims to identify some key antecedents and consequences of opinion leadership in this context. Our results, based on data collected from 808 followers of a fashion focused Instagram account, suggest that originality and uniqueness are crucial factors if a user is to be perceived as an opinion leader on Instagram. In addition, opinion
leadership influences consumer behavioral intentions toward both the influencer (intention to interact in the account and recommend it) and the fashion industry (intention to follow fashion advice posted). Finally, the perceived fit of the account with the consumer's personality strengthens the influence of opinion leadership on the intention to follow published advice. These results have interesting implications for the fashion industry.

DOI:  10. 4185
Introducción. Este artículo analiza el papel de la comunicación personal y los  líderes de opinión en la difusión de tendencias de moda; donde el aumento de  las  colecciones  de  ropa  realizadas  por  las  marcas,  la  democratización  de  la  moda  y  el  creciente  papel  de  la  ‘calle’  para  dictar  tendencias  plantean  comprender mejor dichos procesos de difusión.  Método. Se realiza un análisis  de  las  distintas  aportaciones  científicas  en  el  campo  d e  la  difusión  de  innovaciones  y  las  investigaciones  en  torno  al  liderazgo  de  opinión  y  la  influencia  personal.  Resultados.  El  análisis  identifica  el  auge  de  la  comunicación  personal  y  los  líderes  de  opinión,  los  procesos  de  difusión  de  tendencias  de  moda – que  van  desde  la  imitación  entre  clases  sociales  a  la  propagación viral – , y el liderazgo de opinión e influencia  personal en la moda. Conclusiones .  Este  trabajo  destaca  el  papel  fundamental  que  los  líderes  de  opinión ejercen , a través de la comunicación p ersonal,  para  que una  tendencia  de  moda sea adoptada o rechazada por los consumidores.

Smart cities are conceptualized as a vehicle for sustainable urban development and a means to deliver high quality of life for residents. One of the core functions of a smart city consists in the continuous monitoring of events, assets and people and the use of this information and intelligence for the streamlining of the city's operations. Public opinion represents one type of intelligence of particular importance and value. By monitoring public opinion, governments seek to understand prevalent views about the current events and policies, as well as identify extreme views and trends that may represent problematic situations or precursors to violent actions. Ultimately, maintaining a constant awareness of public opinion means that authorities can better assess and predict public reactions in relation to ongoing events, and thus take appropriate actions to maintain public safety. Due to the popular use of social media to express sentiments and emotions about current events, social media content analysis has been contemplated as a promising solution to capture public opinion. However, existing approaches take a coarse-grained retrospective approach to social media content analysis. Furthermore, those approaches suffer from the lack of scalability and efficiency, as they necessitate the collection and analysis of large volumes of social media content (often millions of posts), to come up with relevant conclusions. In this work, we address those limitations by proposing a novel framework for the real-time monitoring of public opinion. To ensure efficiency and scalability, our framework focuses on the analysis of high impact social media content generated by opinion leaders and their followers as means to offer in-depth insights and sentiment intelligence reports about events, as they are occurring in real time. The proposed framework was implemented and tested on data harvested from 52 economic opinion leaders, with a focus on the China-US trade war as case study. The results show that the convolutional neural network (CNN) classifier used for sentiment analysis yielded a classification accuracy of 86% when differentiating between four sentiment categories: Support, strong support, dissent, and strong dissent. The Support Vector Machine (SVM) classifier employed to perform in-depth emotional analysis attained an accuracy of 82% when differentiating between five emotions: Angry, depressed, excited, happy, and worried. Unlike existing retrospective social media analysis approaches that require the analysis of millions of posts, our approach focuses on the analysis of high-impact social media content in real-time, thus constituting an efficient, sustainable, and timely solution to public opinion monitoring.

By measuring social media users' online activities in terms of information broadcasting, information seeking and relational maintenance, this chapter aimed at investigating how college students' SNS usage patterns may affect their online social network sizes. Statistics in this chapter suggested that more information seeking leads to more Twitter followings while more information broadcasting results in a larger size of Twitter followers. The frequency of relationship management, however, did not predict students' social network size on either Twitter or Facebook. Implications of the study were discussed.

In recent years, the automated, efficient and sensitive monitoring of social networks has become increasingly important for the criminal investigation process and crime prevention. Previously, we have shown that the detection of opinion leaders is of great interest in forensic applications to gather important information. In the current work, it is argued that state of the art methods, determining the relative degree to which an opinion leader exerts influence over the network, have weaknesses if networks exhibit a star-like social graph topology, whereas these topologies result from the interaction of users with similar interests. This is typically the case in networks of political organizations. In these cases, the underlying topologies are highly focused on one (or only a few) central actor(s) and lead to less meaningful results by classic measures of node centrality commonly used to ascertain the degree of leadership. With the help of data collected from the Facebook and Twitter network of a German political party, these aspects are examined and a quantitative indicator for describing star-like network topologies is introduced and discussed. This measure can be of great value in assessing the applicability of established leader detection methods. Finally, two variations of a new measure-the CompetenceRank-which is based on the LeaderRAnk score and aims to address the discussed problems in cases with and without additional network data such as likes and shares, are proposed.

Le commerce équitable (CE) représente une innovation à la fois sociale et commerciale qui, à son stade de développement, doit s’appuyer sur les leviers interpersonnels de diffusion pour conquérir une part plus large du marché. Les comportements de leadership d’opinion, c'est-à-dire de partage d’information et d’influence, sont par conséquent très importants pour accroître la fréquence de choix des produits du CE et leur pénétration dans le tissu social. Dans cet esprit, notre article se préoccupe d’identifier et d’établir le profil des leaders d’opinion du CE. Cet article comporte deux objectifs. Le premier consiste à proposer un outil psychométrique pour faciliter l’identification des leaders d’opinions. Le deuxième objectif est lié à la mise à jour du profil des leaders d’opinion dans le CE. Pour mener à bien ce deuxième objectif nous avons élargi l’approche théorique du leadership d’opinion en l’articulant avec la théorie néo-institutionnaliste, notamment avec les concepts d’entrepreneur institutionnel et de champ organisationnel. L’analyse de la dynamique d’un champ nous a, par ailleurs, conduit à analyser les interactions entre des champs connexes et à définir le concept de « groupe de champs ». L’analyse, basée sur une enquête quantitative, conduit à identifier trois types de leader du CE. Les profils furent établis en fonction des débats qui structurent le champ du commerce équitable, notamment en fonction de la confiance qu’ils accordent ou non aux grandes surfaces pour respecter les principes du commerce équitable. Deux de ces types peuvent être respectivement associés aux grandes filières au sein de CE : la filière intégrée et la filière labellisée. Nous mettons également en lumière un troisième type de leader : des individus susceptibles de jouer le rôle de leaders négatifs, c'est-à-dire de décourager leur entourage d’acheter des produits issus du commerce équitable. Du côté des interactions entre champs l’analyse montre l’interdépendance entre l’agriculture biologique et le CE. Le modèle proposé fait, au final, apparaître un résultat inattendu qui interroge sur la possibilité d’enrichir les modèles de l’intention d’achat par les concepts de connaissance subjective et de confiance.

This paper presents an innovative method design that combines semantic with social network analysis in order to measure opinion leadership in social networking sites in a more accurate way. We used this method to assess the efficacy of the TPM magazine in disseminating its pro-decriminalization of abortion frames (contained in the cover story of its 148th issue) that were associated with the hashtag #precisamosfalarsobreaborto (a trending topic in November 2014). The data were collected from Twitter through the data-mining application NodeXL (N = 1010). A content analysis of a random sample was carried out (N = 376; margin of error = 4%; confidence interval = 95%; Krippendorff’s alpha = 0.661). Using the software Gephi, we plotted the data on a socio-semantic graph, which indicates that (a) the border of the social network does not represent a semantic gap with the center and (b) despite the network being extremely like-minded, one of its hubs appears to be what we conceptualize as a hotspot of contestation. We discuss how future research may replicate and refine our methodology to handle population datasets and big data as well.