Awareness of AAU students on personalized online news: In terms of credibility and diversity of news sources,
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Date
2024-03
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Addis Ababa University
Abstract
People are becoming increasingly reliant on online systems that employ algorithmic curationto organize, select and present information. In the age of digitalization life is full ofalgorithmically recommended news content such as news feeds, recommendations andpersonalizing content recommend. These algorithmically personalization of contents haspotential to influence users’ news consumption behaviors, but users are unaware of theirpresence. This study explored the awareness of AAU students on personalized online medianews in terms of credibility, content, and source diversity. The specific objectives of the studyare to understand awareness of AAU students on the way social media collet user data topersonalize news and prioritize contents, to assess how students judge the credibility of newssources that are recommended automatically on their social media feeds and to assessawareness of AAU students on to what extent they access diverse news content and sources inpersonalized online media. To achieve these objectives, a mixed research design was employedcombining quantitative and qualitative methods to provide a comprehensive understanding ofthe research problem. Also, the study employed a convergent mixed method design, collectingand analyzing both types of data simultaneously. To address the objectives 282 sample sizes ofparticipant surveyed questioners were drawn from total population study selected from variousdepartments and academic years at AAU. Purposive sampling is utilized to select specificparticipants for this research from field of studies given for regular undergraduates, stratifiedsampling techniques was also employed to draw proportional sample size from participantselected using purposive sample by dividing them based on field of study and study years andto different strata and the final participants of the study selected using simple random samplingtechnique. Moreover, in depth interview with set of 8 participants. Key findings from theresearch revealed that while students were generally aware of social media algorithmstracking user activities, they lacked detailed knowledge of the specific types of user datacollected forpersonalizing content. Participants used automatically recommended new usingalgorithm, with varying levels of reliance on personalized news content. Thestudy highlightedthe importance of understanding diverse perspectives and information-seeking behaviorsamong students from different academic backgrounds in personalized news environment.
Keywords: Social media algorithm, Personalization, Filter Bubble, Echo chamber, News
Feeds, Credibility, News Source Diversity
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Keywords
Social media algorithm, Credibility, News Source Diversity