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dc.contributor.authorPreisendorfer, Matthew
dc.contributor.authorSengupta, Sam; Adviser
dc.contributor.authorWhite, Joshua; Adviser
dc.contributor.authorTekeoglu, Ali; Adviser
dc.descriptionA Master’s Thesis Presented to Department of Computer and Information Sciences SUNY Polytechnic Institute Utica, New York In Partial Fulfillment Of the requirements for the Master of Science Degree.en_US
dc.description.abstractTwitter is an ever-growing social-media platform where users post tweets, or small messages, for all of their followers to see and react to. This is old news of course, as the platform first launched over ten years ago. Currently, Twitter handles approximately six thousand new tweets every second, so there is plenty of data to be analyzed. With a character limit of 140 per tweet, emojis are commonly used to express feelings in a tweet without using extra characters that more explaining might use. This is helpful in identifying the mood or state of mind that a person may have been in when writing their tweet. From a computing standpoint, this makes mood analysis much easier. Rather than analyzing a group of words and predicting moods from keywords, we can analyze single (or many) emoji(s), and then match those emojis to commonly expressed emotions and feelings. The objective of this research is to gather large amounts of Twitter data and analyze emojis used to find correlations in societal interactions, and how current events may drive social media interactions and behaviors. By creating topic models for each user and comparing it with the emoji distribution analysis, a trust ”fingerprint” can be created to measure authenticity or genuineness of a given user and/or group of users. The emoji distribution analysis also provides the possibility of demographic predictions. Analysis is not limited to Twitter of course but is used here because the API is free and generally easy to use. This paper aims to prove the validity of emoji analysis as a method of user identification and how their trust models can be used in conjunction with pre-existing models to improve success rates of these models.en_US
dc.subjectSocial Mediaen_US
dc.subjectTrust Modelingen_US
dc.subjectDemographic Predictionsen_US
dc.subjectMood Analysisen_US
dc.titleSocial Media Emoji Analysis, Correlations and Trust Modelingen_US
dc.description.institutionSUNY Polytechnic Institute

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  • SUNY Polytechnic Institute College of Engineering
    This collection contains master's theses, capstone projects, and other student and faculty work from programs within the Department of Engineering, including computer science and network security.

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