• The Science & Technology
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• s.513
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• pp.92-95
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• 2012

• The Science & Technology
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• s.497
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• pp.84-87
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• 2010

• The Science & Technology
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• s.543
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• pp.108-111
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• 2014

• Journal of Information Science Theory and Practice
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• v.10 no.2
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• pp.86-101
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• 2022

This research aimed at studying the factors that influence new media exposure of political news by youths in Isan society in Thailand. The target group comprised 1,200 individuals, obtained from multi-stage sampling from undergraduate students in Isan's autonomous universities, governmental universities, and private institutions. The data collection tool was a questionnaire, the content of which was validated by experts. The reliability of the tool was tested by the formula for Cronbach's alpha coefficient, which yielded a reliability of 0.83. Multiple regression analysis was applied to analyze the data. The results, regarding factors influencing the channels for political news exposure, showed that channels for political news exposure were mostly influenced by inner drives, followed by importance in political news exposure, influence from social networks, and specific characteristics of the Internet. This could explain the variation of channels for political news exposure at 46.5%. In terms of factors influencing political news selection, it was found that political news selection was influenced mostly from social networks, followed by inner drives, benefits from political news exposure, specific characteristics of the Internet, and the field of study. The variation of the political news selection could be explained at 44.6%. These results elaborate on the current situation in Thailand, especially in Isan region, where youths in higher education are playing an increasing role in demonstrating their political stance through various political activities.

• Journal of Scientific & Technological Knowledge Infrastructure
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• s.16
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• pp.6-11
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• 2004

• International Journal of Computer Science & Network Security
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• v.21 no.2
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• pp.229-237
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• 2021

Our modern 'information-hungry' age demands delivery of information at unprecedented fast rates. Timely delivery of noteworthy information about recent events can help people from different segments of life in number of ways. As world has become global village, the flow of news in terms of volume and speed demands involvement of machines to help humans to handle the enormous data. News are presented to public in forms of video, audio, image and text. News text available on internet is a source of knowledge for billions of internet users. Urdu language is spoken and understood by millions of people from Indian subcontinent. Availability of online Urdu news enable this branch of humanity to improve their understandings of the world and make their decisions. This paper uses available online Urdu news data to train machines to automatically categorize provided news. Various machine learning algorithms were used on news headline for training purpose and the results demonstrate that Bernoulli Naïve Bayes (Bernoulli NB) and Multinomial Naïve Bayes (Multinomial NB) algorithm outperformed other algorithms in terms of all performance parameters. The maximum level of accuracy achieved for the dataset was 94.278% by multinomial NB classifier followed by Bernoulli NB classifier with accuracy of 94.274% when Urdu stop words were removed from dataset. The results suggest that short text of headlines of news can be used as an input for text categorization process.

• Journal of Scientific & Technological Knowledge Infrastructure
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• s.17
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• pp.14-17
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• 2005

• Journal of Scientific & Technological Knowledge Infrastructure
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• s.13
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• pp.7-13
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• 2004

• Journal of Scientific & Technological Knowledge Infrastructure
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• s.27
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• pp.8-13
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• 2007

• Journal of Scientific & Technological Knowledge Infrastructure
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• s.21
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• pp.14-15
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• 2006