Connecting Opinions to Opinion-Leaders: A Case Study on Brazilian Political Protests

Rocha, Leonardo; Mourão, Fernando; Vieira, Ramon; Neves, Alan C.; Carvalho, Dárlinton Barbosa Feres; Bandyopadhyay, Bortik; Parthasarathy, S.; Ferreira, Renato

doi:10.1109/dsaa.2016.77

Cited by 5 publications

(4 citation statements)

References 39 publications

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“…However, vitality is a doubleedged sword that can also amplify negative publicity, fuel controversy, and undermine a leader's credibility. The popularity of political leaders in informal communities is subject to constant flux and volatility, reflecting the dynamic nature of public opinion [17] [18]. Trends, events, and crises can profoundly impact the popularity trajectories of political leaders, leading to sudden surges or declines in support.…”

Section: Figure-4-rating World Leaders Through Samentioning

confidence: 99%

“…The popularity of political leaders in informal communities is a complex and multifaceted phenomenon shaped by a myriad of factors, including communication strategies, media representation, socio-economic contexts, and digital technologies. Informal communities serve as vibrant arenas where public opinion is formed, contested, and negotiated in real time [19] [2]. Understanding the dynamics of popularity within these communities requires a nuanced appreciation of the interactions between individuals, technology, and broader socio-political dynamics.…”

Section: Figure-4-rating World Leaders Through Samentioning

confidence: 99%

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Artificial Intelligence-Powered Sentiment Analysis Tool to Assess the Popularity of Political Leaders in Informal Communities

Rakesh Kumar Yadav

2024

jes

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This study investigates the utility of Artificial Intelligence sentiment analysis tools in assessing the popularity of political leaders within informal communities. In contemporary socio-political landscapes, understanding public sentiment towards political figures is paramount for gauging societal perceptions and predicting electoral outcomes. Leveraging sentiment analysis techniques, this research delves into the nuanced expressions and sentiments prevalent in informal online communities regarding political leaders. By analyzing textual data from social media platforms, forums, and other digital sources, the study aims to discern patterns and trends indicative of public opinion dynamics. Through natural language processing and machine learning algorithms, sentiment analysis tools enable the automated extraction of sentiments, emotions, and attitudes towards political leaders, providing valuable insights into their perceived popularity and public reception. The findings contribute to our understanding of the intersection between technology, politics, and public opinion, shedding light on the evolving landscape of political discourse in digital spaces. The study underscores the significance of sentiment analysis for political analysts, policymakers, and researchers in comprehending and navigating the complexities of contemporary political environments.

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Section: Figure-4-rating World Leaders Through Samentioning

confidence: 99%

Section: Figure-4-rating World Leaders Through Samentioning

confidence: 99%

Artificial Intelligence-Powered Sentiment Analysis Tool to Assess the Popularity of Political Leaders in Informal Communities

Rakesh Kumar Yadav

2024

jes

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“…For the business and marketing domain, opinion leaders are able to increase the profits of companies since they can attract potential customers. 7,8 For the political science domain, different works 9,10 were interested in the study of political posts and then to the detection of opinion leaders in this field. Those latter can help a political trend to promote his electoral campaign.…”

Section: Opinion Leader Definition and Rolementioning

confidence: 99%

Opinion leaders' detection in dynamic social networks

Oueslati

Arrami

Dhouioui

et al. 2020

Concurrency and Computation

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Summary Social media networks have revolutionized the way users interact and express their opinion. Obviously, identifying opinion leaders has a widespread applicability. For instance, by detecting leaders, companies can manipulate the public opinion. However, this task is challenging due to the complexity and the ceaseless change of the social networks structure. Yet, existing opinion leaders 'detection methods have essentially focused on static social graphs neglecting the temporal characteristics. Therefore, the necessity of identifying opinion leaders seems to be more and more crucial. In this context, we present a new approach for detecting opinion leaders based on analyzing online community interactions and dealing with the dynamic aspect of social networks. The experiments are performed on real data and the comparison of the proposed approach with commonly used approaches showed a good performance.

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“…Risselada et al find that degree centrality indicates opinion leadership, but that self-reported opinion leadership indicates opinion leadership only under the right social circumstances [24]. Rocha et al present a preliminary evaluation about a combined analysis of Sentiment Analysis and Influential Users Detection and propose a methodology to quantify factors in real domains that may affect such analysis, as well as the potential benefits of combining Sentiment Analysis Methods with Influential Users Detectionones [25]. Song et al examine the extent to which openness, exhibitionism, and competence in interpersonal relationships and flow experience affect the propensity of opinion leadership and opinion leadership behavior in the context of the travel industry and the results indicate that openness, exhibitionism, and competence in interpersonal relationships significantly increase opinion leadership [26].…”

Section: B Opinion Leader Rolementioning

confidence: 99%

High-Value User Identification Based on Topic Weight

et al. 2019

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The role identification in social networks is important for analyzing and understanding social networks, predicting user behavior, and researching relationships and interactions between users. Most role identification works are based on link analysis following the ideas of PageRank and HITS, and some methods also combing link analysis and content analysis. However, previous role identification methods focus on the amount of topic released by the target user without considering the importance of the topic. In order to identify high-value users by topic weights in social networks, this paper first proposes a method of assigning topic weights based on the LDA model and the collective credit allocation method in science(CCA). Then we introduce content value density based on the Kullback-Leibler divergence of topic weight and topic distribution to rank the high-value users. In addition, the new method is more suitable for calculation on large-scale networks and time-evolution networks because the new method is an inductive method which works on the local graph. Finally, the experiments are carried out to analyze the effect of the topic weight allocation and content value density in a real data set. The results show that the new method is superior to the compared methods when identifying high-value users in certain circumstances. INDEX TERMS Social network, role identification, high-value user, topic weight.

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Connecting Opinions to Opinion-Leaders: A Case Study on Brazilian Political Protests

Cited by 5 publications

References 39 publications

Artificial Intelligence-Powered Sentiment Analysis Tool to Assess the Popularity of Political Leaders in Informal Communities

Artificial Intelligence-Powered Sentiment Analysis Tool to Assess the Popularity of Political Leaders in Informal Communities

Opinion leaders' detection in dynamic social networks

High-Value User Identification Based on Topic Weight

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