Social Media Mining for Affective and Business Cues

This thesis centers around social media and the content people generate. We utilized Artificial Intelligence and Natural Language Processing techniques to address five distinct challenges. Firstly, we developed a system capable of detecting instances where individuals copy or borrow ideas from others' work. Secondly, we constructed a system for summarizing narratives. We also explored the correlation between images and the comments users post about them. Another project involved investigating barriers impacting business interactions between Russia and Finland. Lastly, we devised a method to identify social media users who may harbor harmful intentions or plans towards others, akin to unfortunate incidents in New Zealand and Orlando.

All the research findings on these topics were disseminated through significant conferences and journals. Through this work, we came to recognize that social media serves as a substantial information source, albeit a complex one. Extracting crucial information from the midst of this digital cacophony necessitates a blend of social acumen and technical proficiency. Social media serves as a reflective mirror of our daily thoughts, preferences, and actions, making its comprehension all the more imperative.