Exploring Government Uses of Social Media through Twitter Sentiment Analysis

Chen, Hsuanwei Michelle; Franks, Patricia C.; Evans, Lois K.

doi:10.6025/jdim/2016/14/5/290-301

Cited by 16 publications

(3 citation statements)

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“…First, results for the complete set of users are presented in Table 14, and then compared with the set of bot users (Table 15). Finally, if the corpus is analyzed by community of active user, it is possible to extract some interesting insights present on Table A12: the three independentist parties (ERC, JxCat and CUP) have the higher positive rate (above 38%), a fact that coincides with other studies, where the government ruling parties have a higher positive sentiment rate (Chen et al, 2016). On the other hand, the 3 constitutionalist parties (VOX, Cs and PP) have the higher negative rates (more than 50%), which correlates with their aggressive style of opposition to the government.…”

Section: 33) Tweets Content Analysissupporting

confidence: 70%

Overview of the Twitter conversation around #14F 2021 Catalonia regional election: analysis of echo chambers and presence of social bots

Cervera,

Pérez-Solà,

Batlle

2023

Preprint

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The omnipresence of the digital ecosystem makes it increasingly important in our societies, which implies that the analysis and study of the digital battlefield in political elections is also becoming more necessary to protect our democracies. Previous literature showed the existence of information operations around the world, designed to manipulate the political perception of citizens, and therefore, the electoral results. This paper examines the Twitter conversation around #14F 2021 Catalonia regional elections, which had special significance due to the pandemic situation and the highly polarized scenario around Catalonia and Spain, using tools and techniques from Big Data Analytics and Artificial Intelligence. The results obtained show that the conversation existed inside robust echo chambers within each political party community, which became even more powerful if parties are unified into political affinity blocks. Also, focusing on the analysis related to the social bot presence, a significant quantity of results showed a higher presence of social bots in VOX party community compared to the rest of communities. This study corroborates other existing studies regarding the Catalan and Spanish scenario on the presence of echo chambers and on the existence of social bots with their tendency to basically amplify content; it also uncovers the lack of existence of cross-conversation between the independentist and unionist political block claimed in other studies.

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Section: 33) Tweets Content Analysissupporting

confidence: 70%

Overview of the Twitter conversation around #14F 2021 Catalonia regional election: analysis of echo chambers and presence of social bots

Cervera,

Pérez-Solà,

Batlle

2023

Preprint

View full text Add to dashboard Cite

show abstract

“…It is followed by Guidance (24.6%) and Interaction (22.5%). The smallest posting type is Information disclosure (34, 3.6%), and user responses were also the least (3,294). For Chengdu, the type with the largest posting volume is Guidance as in Shanghai, with 713 posts (32.6%), and 67,072 user responses obtained.…”

Section: Rq1 and Rq2: Differences In Posts And User Responsesmentioning

confidence: 99%

Municipal Government Use of Social Media: An Analysis of Three Chinese Cities

Gu¹,

Harrison²,

Zhu³

2020

Proceedings of the Annual Hawaii International Conference on System Sciences

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To investigate the use of information dissemination and public communication by Chinese municipal governments, we analyzed the social media use of three large cities with relatively mature social media development: Shanghai, Nanjing and Chengdu. We collected 4,429 government posts and users' likes, shares and comments from Weibo accounts of each city's information office. Government posts were coded into 7 types and 16 topics. We used cross-tabulation, correlation analysis and multivariate linear regression to analyze government posts, user responses and their inter-relationships. Chengdu issued the most posts; Nanjing received the highest average user response to posts and exhibited the best signs of success in communication between the government and citizens. IntroductionThe emergence of social media has triggered a change in communication with a considerable impact on developing countries such as China. At present, social media have become one of the most important sources of information for many Chinese, especially young people, and also a new channel of communication for government information disclosure, image construction, and public dialogue.Among the first and still most important social media channels to appear in China is Weibo. By the end of 2018, Weibo's users have exceeded 350 million, an increase of 10.9% over the previous year, and the usage rate by Internet users has reached 42.3%. The Chinese government opened an account on Weibo at the end of 2009. So far, it has developed more than 130,000 certified Weibo government channels, involving national, provincial, municipal and county  *This work was also supported by the China Scholarship Council and CTG, University at Albany, SUNY.

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“…Importantly, unlike Naive Bayes, maximum entropy makes no assumptions about the relationships between features, and potentially perform better when conditional independence assumptions are not met. The λ i,c 's are feature-weight parameters; inspection of the definition of PME shows that a large λ i,c means that f i is considered a strong indicator for class c. The underlying philosophy is that we should choose the model that makes the fewest assumptions about the data while still remaining consistent with it, which makes intuitive sense (Chen et al, 2016).…”

Section: Machine Learning Techniquementioning

confidence: 99%

Sentiment Analysis Techniques to Analyze Hse Situational Awareness at Oil and Gas Platforms Using Machine Learning

Dafaallah¹,

Hashim²

2020

INDJCSE

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Health Safety & Environment (HSE) situational awareness is a very important aspect of any risky workplace. Negligence in complying with HSE policies and practices might lead to unwanted incidents, critical injuries, death, spread of diseases and environmental pollution. In most corporations, information on HSE related incidents is disseminated through formal channels such as reports. Employees on the other hand frequently use social media to share, complain and discuss HSE-related issues. The issues are discussed through an informal platform, it is difficult to analyze opinions for further action. Therefore, this study will investigate existing sentiment analysis models and formulate a suitable sentiment analysis model using machine learning technique. Through literature review, Naïve Bayes model was found to be the most efficient text classification in sentiment analysis. This technique still needs further enhancement as the accuracy is not within requirement. Upon enhancing the Naïve Bayes model, a better outcome can be attained.

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Exploring Government Uses of Social Media through Twitter Sentiment Analysis

Cited by 16 publications

References 0 publications

Overview of the Twitter conversation around #14F 2021 Catalonia regional election: analysis of echo chambers and presence of social bots

Overview of the Twitter conversation around #14F 2021 Catalonia regional election: analysis of echo chambers and presence of social bots

Municipal Government Use of Social Media: An Analysis of Three Chinese Cities

Sentiment Analysis Techniques to Analyze Hse Situational Awareness at Oil and Gas Platforms Using Machine Learning

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