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  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.
1

Qualitative experiences of personalization algorithms : The strategies used by university students (to counteract ideological homogeneity) when navigating social media feeds and their experience with personalization algorithms. / Kvalitativa erfarenheter av personifieringsalgoritmer

Bjernersjö, Mathilda, Wikdahl, Alicia January 2020 (has links)
Purpose – At the beginning of this year the number of social media user worldwide was recorded to be at 3.80 billion and is estimated to keep rising (Kemp, 2020). Along with this connectivity, new concepts have been acknowledged that have caused some controversy. These concepts have been named echo chambers and filter bubbles. This study investigates how social media users experience personalisation algorithms and the perceived existence of filter bubbles and echo chambers on various social media platforms and channels. Furthermore, it explores to what extent these users make use of strategies when navigating their social media feeds and if they believe that said strategies have any effect on personalisation algorithms, filter bubbles, and echo chambers. Method – Semi-structured interviews were conducted with 10 participants currently studying at Jönköping University. The interviews were then transcribed and analysed using a thematic approach.  Findings – The perception of students at Jönköping University is that filter bubbles and echo chambers exist in a varying degree of visibility and commonality depending on the situation and medium. These are also experienced to be interconnected with personalisation algorithms. The experience of personalisation algorithms is mainly positive, as it allows the users to easily navigate the feed but negative implications are also mentioned.  These are isolation, the negative strengthening and creation of filter bubbles or echo chambers. However, the algorithm is not believed to be the only thing responsible for the creation. The users believe that their own navigation and strategies can have such an effect. The strategies that users use are (1) Passive navigation, (2) Source evaluation, (3) Multiple source searching, (4) Responsibility taking, and the (5) Creation of filter bubbles. The effect these have on filter bubbles, personalisation algorithms and echo chambers is helping, to an extent, alleviate some of the negative effects that these are experienced to have. However, the passive navigation and creation of filter bubbles have a somewhat opposite effect as they help the creation or maintaining of filter bubbles and personalisation algorithms. Implications – The findings in this study build on existing evidence of echo chambers being more easily formed when the topic of discussion is of a political nature, as it is described by Barberá, Jost, Nagler, Tucker, and Bonneau (2015) in their research on how echo chambers form on Twitter. Furthermore, these findings could be considered to disagree with the research of Dubois and Blank (2018) who found that people who had a high political interest were less likely of ending up in an echo chamber. Although, due to their research taking all media into account, such as television, radio, newspaper, and so forth, while the current study is focused on social media alone the comparison is a bit more difficult to make. Finally, in the study made by Seargeant and Tagg (2019), it was concluded that the personalisation algorithms are not the sole contributor to filter bubbles forming on Facebook and that the users play a key role in how their online environment is shaped. The results of this study build on their research that the users do use strategies when navigating that affect what is being shown to them on their social media feeds. However, the participants of the current study claimed that the algorithms played a pretty large role too, which is not quite in line with the research conducted by Seargeant and Tagg (2019). Limitations – The generalisability of this study is limited due to the small sample size chosen to conduct this study, although instead, it provides deeper insight into the relationship between humans and their social media platforms and channels. Moreover, a bias that should be acknowledged is that when conducting interviews there is a risk of encountering response bias, which is when the participants assume the purpose of the study and adapt their answers to fit what they believe the researcher(s) want to hear. To avoid this precautions were taken when designing the questions to make sure that they would not lead the participants in any directions.

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