Master Degree / Yüksek Lisans Tezleri
Permanent URI for this collectionhttps://hdl.handle.net/11147/3008
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Master Thesis Identifying Communities Using Collaboration and Word Association Networks in Turkish Social Media(Izmir Institute of Technology, 2018) Atay, Abdullah Asil; Tekir, SelmaSocial media contents are always very attractive title for researchers. Scores of people use social media and share their ideas with pictures, videos or documents. Researchers analyze this information and they try to deduce beneficial data. A lot of researchers think that analyzing social media information is a very important research area. There are a lot of social media platforms which have Turkish contents. We can give an example Ekşisözlük which have Turkish contents and popular social media platform in Turkey. Within scope of the thesis, Ekşisözlük contents downloaded, decomposed and used actively. Social media consists of human or human made products and sharing contents have some similarities. In this thesis, to calculate similarities, some methods are used. Scope of the thesis, two different networks are created from same content which are word association network and collaboration network. Word association network is a network that created by coexistence of words in specific window size. Collaboration network is a network that created by entered content to same title with different users. This information gives the similarity of users. These two networks are analyzed separately and deduced some information.Master Thesis Analyzing Social Media Data by Frequent Pattern Mining Methods(Izmir Institute of Technology, 2018) Güvenoğlu, Büşra; Ergenç Bostanoğlu, Belgin; Ergenç Bostanoğlu, BelginData mining is a popular research area that has been studied by many researchers and focuses on finding unforeseen and important information in large dataset. Social media data is one of the most popular and large heterogeneous data collected from social networking sites, microblogs, photo or video sharing sites. Social media represents the entities and their relations. One of the popular data structures used to represent large heterogeneous data in the field of data mining is graphs. The nodes of a graph represent entities and the edges of a graph represent the relations between the entities. So, graph mining is one of the most popular subdivisions of data mining. A frequent pattern is referred to as pattern that is more frequently encountered than the user-defined threshold in a dataset. Frequent patterns in a dataset can give important information about dataset. Using this information, data can be classified or clustered. Frequent patterns can provide different perspective on social media data with respect to sociology, consumer behaviour, marketing, communities. In this thesis, popular frequent pattern mining algorithms have been examined and it has been observed that most algorithms are not suitable for large datasets. Since data in today’s world, especially social networks, has very large data, the existing pattern mining algorithms are not suitable for this data. The aim of this thesis is to implement an existing frequent pattern mining algorithm in parallel manner and to find frequent patterns in a social media data.Master Thesis Matching of Social Media Accounts by Using Public Information(Izmir Institute of Technology, 2016) Çetinkal, Yağız; Şahin, Serap; Şahin, SerapProtection of private information on social networks (SNs) has become a serious and important topic since social network sites became popular and widely adopted worldwide. Usually people want their personal information to be known only by a small group of people including close friends and families. But sometimes they willingly accept to give some particular information about themselves to individuals which are neither a friend nor an acquaintance. Each SN has different purposes and people subscribe many of them. However, public information available on these sites reveals many aspects of user’s identity. In this work, it is shown that public information can be used to detect the different accounts of the same individual. This study is performed on two popular social media sites: Twitter and Facebook. Public attributes of the profiles such as real name, user name and status updates (tweets and posts) are used for comparing profiles on two SNs. Different data mining algorithms are compared for matching profiles. Also relationship between text similarity and total term counts of status updates is analyzed. Results show that simple features like real names, user names and status updates have high similarity between the accounts of the same users and these features can be used to detect profiles of the same user on different SNs. Also the more status updates a user posts on Facebook the more he will likely be detected by the matching schema. Thus, public information can be exploited to pose a threat to the privacy of the people on the Internet.