PoliticAlly: Finding political friends on twitter

Jain, Suchita; Sharma, Vanya; Kaushal, Rishabh

doi:10.1109/ants.2015.7413659

Cited by 6 publications

(3 citation statements)

References 1 publication

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“…A study by Jain et al [68] proposes a system to find politically similar friends for Twitter users based on their interactions on Twitter. It develops a recommendation algorithm that uses relatedness measures to construct a graph between different users.…”

Section: ) Advocating/influencer Usersmentioning

confidence: 99%

A Survey on Computational Politics

et al. 2020

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Computational Politics is the study of computational methods to analyze and moderate users' behaviors related to political activities such as election campaign persuasion, political affiliation, and opinion mining. With the rapid development and ease of access to the Internet, Information Communication Technologies (ICT) have given rise to massive numbers of users joining online communities and the digitization of political practices such as debates. These communities and digitized data contain both explicit and latent information about users and their behaviors related to politics and social movements. For researchers, it is essential to utilize data from these sources to develop and design systems that not only provide solutions to computational politics but also help other businesses, such as marketers, to increase users' participation and interactions. In this survey, we attempt to categorize main areas in computational politics and summarize the prominent studies in one place to better understand computational politics across different and multidimensional platforms. e.g., online social networks, online forums, and political debates. We then conclude this study by highlighting future research directions, opportunities, and challenges.

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Section: ) Advocating/influencer Usersmentioning

confidence: 99%

A Survey on Computational Politics

et al. 2020

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“…The paper [8] presents a study where the authors exploit tweets to find out political friends. They named their approach a "Politic Ally" which identifies the friends having the same political interest.…”

Section: Politicsmentioning

confidence: 99%

“…Due to the importance it has gained, Twitter inspired novel researches concerned with many areas of computer science, in particular data mining [2], sentiment analysis [3], text mining [4], discovering mobility of people [5][6][7] and so on. For example, tweets are analyzed to find out political friends [8], so this implies that texts are analyzed to detect their political polarity. Another interesting application is detecting communities from networks of users [9], in which sentiment analysis plays an important role; sentiment analysis and opinion mining can be also adopted to study the general sentiment of a given country [10], in order to detect the degree of support to terrorists.…”

Section: Introductionmentioning

confidence: 99%

Towards the Discovery of Influencers to Follow in Micro-Blogs (Twitter) by Detecting Topics in Posted Messages (Tweets)

et al. 2020

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Micro-blogs, such as Twitter, have become important tools to share opinions and information among users. Messages concerning any topic are daily posted. A message posted by a given user reaches all the users that decided to follow her/him. Some users post many messages, because they aim at being recognized as influencers, typically on specific topics. How a user can discover influencers concerned with her/his interest? Micro-blog apps and web sites lack a functionality to recommend users with influencers, on the basis of the content of posted messages. In this paper, we envision such a scenario and we identify the problem that constitutes the basic brick for developing a recommender of (possibly influencer) users: training a classification model by exploiting messages labeled with topical classes, so as this model can be used to classify unlabeled messages, to let the hidden topic they talk about emerge. Specifically, the paper reports the investigation activity we performed to demonstrate the suitability of our idea. To perform the investigation, we developed an investigation framework that exploits various patterns for extracting features from within messages (labeled with topical classes) in conjunction with the mostly-used classifiers for text classification problems. By means of the investigation framework, we were able to perform a large pool of experiments, that allowed us to evaluate all the combinations of feature patterns with classifiers. By means of a cost-benefit function called “Suitability”, that combines accuracy with execution time, we were able to demonstrate that a technique for discovering topics from within messages suitable for the application context is available.

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Sentiment Analysis on Predicting Presidential Election: Twitter Used Case

Barghuthi

Said

2020

Intelligent Computing Systems

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PoliticAlly: Finding political friends on twitter

Cited by 6 publications

References 1 publication

A Survey on Computational Politics

A Survey on Computational Politics

Towards the Discovery of Influencers to Follow in Micro-Blogs (Twitter) by Detecting Topics in Posted Messages (Tweets)

Sentiment Analysis on Predicting Presidential Election: Twitter Used Case

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