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Open AccessArticle

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

1
Department of Management, Information and Production Engineering, University of Bergamo, 24129 Bergamo, Italy
2
Faculty of Computing & IT, University of Sialkot, Sialkot 51040, Pakistan
*
Author to whom correspondence should be addressed.
Appl. Sci. 2020, 10(16), 5715; https://doi.org/10.3390/app10165715
Received: 8 July 2020 / Revised: 4 August 2020 / Accepted: 12 August 2020 / Published: 18 August 2020
(This article belongs to the Special Issue Applied Machine Learning)
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. View Full-Text
Keywords: social media; micro-blogs (Twitter); towards recommending influencers based on topic classification; investigation framework; comparison of various techniques for topic classification; cost-benefit function social media; micro-blogs (Twitter); towards recommending influencers based on topic classification; investigation framework; comparison of various techniques for topic classification; cost-benefit function
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Ali, M.; Baqir, A.; Psaila, G.; Malik, S. Towards the Discovery of Influencers to Follow in Micro-Blogs (Twitter) by Detecting Topics in Posted Messages (Tweets). Appl. Sci. 2020, 10, 5715.

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