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  • 1.
    Arhammar Andersson, Molly
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM).
    Tillgänglighet, testning och förtroende: eller konsten att inte vara ett funktionshinder2018Independent thesis Basic level (university diploma), 5 credits / 7,5 HE creditsStudent thesis
    Abstract [sv]

    Arbetet undersöker hur Sveriges kommuner valt att testa tillgänglighetsanpassningen av sina webbplatser och vilket förtroende de har för att webbplatsen är tillgänglighetsanpassad. Dessa två faktorer analyseras sedan i relation till varandra för att belysa om, och i så fall på vilket sätt, testpraktik påverkar förtroende. Resultatet ställs i kontrast mot den tidigare forskning som säger att manuell testning av tillgänglighetsanpassning är mer pålitlig än automatisk sådan, samt att testning överlag leder till högre förtroende. Undersökningen görs genom en enkätundersökning utskickad till alla Sveriges kommuner och kommer fram till att det, i samklang med tidigare forskning, går att se att närvaro av testning leder till högre förtroende, medan det i motsats till vad forskningen indikerar inte går att se att de som testar manuellt har ett generellt högre förtroende än de som testar automatiskt. Detta leder till slutsatsen om att datavetenskapen måste kommunicera ut sin kunskap om hur testning av tillgänglighetsanpassning kan utföras på bästa sätt, att kommunerna själva behöver få tillgång till mer kunskap om hur man testar effektivt och pålitligt med manuell testning, och pekar mot vidare forskning inom datavetenskap, filosofi och psykologi kring testning, förtroende och människans relation till maskinen.

  • 2.
    Arhammar Andersson, Molly
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM).
    What we talk about when we talk about winners: Using clustering of Twitter topics as a basis for election prediction2019Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
    Abstract [en]

    Social media has over the years partly become a platform to express opinions and discuss current events. Within the field of Computer Science, Twitter has been used both as the basis for political analysis - for example using sentiment analysis to predict election results - and within the field of cluster analysis, where the question of how to best design and use an algorithm to extract topics from tweets has been studied. The ClusTop algorithm is specifically designed to cluster tweets based on topics. This paper aims to explore whether it is possible to (a) use an implementation of the ClusTop algorithm to identify topics connected to tweets about Trump and Clinton just before the American 2016 election, and (b) distinguish between the topics used in connection with a specific candidate in states where they won versus states where they lost the election. The problem is approached through the method of a controlled experiment where the data collected from Twitter is divided into groups and run through the ClusTop algorithm. The topics are then compared to draw tentative conclusions about their validity as a basis for election prediction. The study finds that it is indeed possible to adapt the ClusTop algorithm to use with tweets and geolocation to identify different topics, thus confirming the usefulness of the algorithm. In addition to this, the study confirms that manually examining the words used within the topics makes it possible to see differences between them. The work thereby places itself in the tradition of exploring how Twitter can be used for election prediction by being one of the first studies to look at clustering as a way of approaching the problem.

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