Computer Engineering / Bilgisayar Mühendisliği

Permanent URI for this collectionhttps://hdl.handle.net/11147/10

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Author: search.filters.author.Karataş, ArzumSubject: search.filters.subject.Online Social Network

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  • Conference Object
    A Review on Social Bot Detection Techniques and Research Directions
    (Bilgi Güvenliği Derneği, 2017) Karataş, Arzum; Şahin, Serap
    The rise of web services and popularity of online social networks (OSN) like Facebook, Twitter, LinkedIn etc. have led to the rise of unwelcome social bots as automated social actors. Those actors can play many malicious roles including infiltrators of human conversations, scammers, impersonators, misinformation disseminators, stock market manipulators, astroturfers, and any content polluter (spammers, malware spreaders) and so on. It is undeniable that social bots have major importance on social networks. Therefore, this paper reveals the potential hazards of malicious social bots, reviews the detection techniques within a methodological categorization and proposes avenues for future research.
  • Conference Object
    Citation - Scopus: 2
    A Comparative Study of Modularity-Based Community Detection Methods for Online Social Networks
    (CEUR Workshop Proceedings, 2018) Karataş, Arzum; Şahin, Serap
    Digital data represent our daily activities and tendencies. One of its main source is Online Social Networks (OSN) such as Facebook, YouTube etc. OSN are generating continuously high volume of data and define a dynamic virtual environment. This environment is mostly represented by graphs. Analysis of OSN data (i.e.,extracting any kind of relations and tendencies) defines valuable information for economic, socio-cultural and politic decisions. Community detection is important to analyze and understand underlying structure and tendencies of OSNs. When this information can be analysed successfully, software engineering tools and decision support systems can produce more successful results for end users. In this study, we present a survey of selected outstanding modularity-based static community detection algorithms and do comparative analysis among them in terms of modularity, running time and accuracy. We use different real-world OSN test beds selected from SNAP dataset collection such as Facebook Ego network, Facebook Pages network (Facebook gemsec), LiveJournal, Orkut and YouTube networks.