Nadejda Komendantova is at the forefront of exploring this promising field, having recently utilized an innovative tool, piloted last year, to scrutinize the data from X, with particular focus on risk management, earthquakes, and disasters in general. The results of the study have been featured in Machine Learning and Knowledge Extraction, which is a top journal in the area of on machine learning and applications.
In the era of digital communication, myriads of social media and web platforms can be used as vital sources of data, which can help us explore the patterns of how public opinion and reactions are shaped, how new trends emerge, and how the market dynamics work.
Everyone uses social media. Whether you are on Facebook, Instagram, X (formerly Twitter), or any other platform — we are all contributing to a large collective pool of data. A lot of this data is fun, but completely useless: cat pictures, competitive eating videos, wilderness survival tips... But quite a lot of seemingly useless data could actually help us understand how our interactions online shape the society in a broader sense.
In the recent decades the landscape of online networks has changed drastically, largely due to the popularization of social media platforms. Recent estimates show that an average internet user has at least 8 different social media accounts on different platforms. But why do we like them so much? Social media platforms are more open and creative than other corners of the internet, giving us the flexibility to express ourselves in a more natural manner. One can say they redefined the way we interact online, mimicking real-life interactions. Nowadays, online platforms play a vital role in our society by providing an open space where people can freely share information, express their opinions on events, policies, and other topics, thereby inevitably shaping public discourse and influencing societal trends.
Social intelligence mining is the process of obtaining big data from user-generated content, such as social media posts, to identify social trends or form better understanding of public sentiments and communication patterns. Utilizing this new method could help empower businesses, organizations, and researchers to harness the vast amount of data available on social media platforms to make informed decisions, manage their brand, engage with their audience, and adapt to a rapidly changing digital landscape. Being both promising and innovative as an emerging source of big data, social intelligence mining could not have missed the interest of researchers.
Nadejda Komendantova participates in the study about social intelligence mining and unlocking insights from X. Social trend mining, situated at the confluence of data science and social research, provides a novel lens through which to examine societal dynamics and emerging trends. This paper explores the intricate landscape of social trend mining, with a specific emphasis on discerning leading and lagging trends. Within this context, our study employs social trend mining techniques to scrutinize X (formerly Twitter) data pertaining to risk management, earthquakes, and disasters. A comprehensive comprehension of how individuals perceive the significance of these pivotal facets within disaster risk management is essential for shaping policies that garner public acceptance. This paper sheds light on the intricacies of public sentiment and provides valuable insights for policymakers and researchers alike. The results of the study have been featured in Machine Learning and Knowledge Extraction, which is a top journal in the area of on machine learning and applications. The study is available at: https://pure.iiasa.ac.at/id/eprint/19245/1/make-05-00093.pdf