Speaker: Dr. Nicolas Turenne

Time: 2:00-3:00p.m., 27 Nov 2019 (Wed)

Venue: T2-202

Language: English

Abstract:

Internet produces lots of information, and text information contributes mainly to disseminate knowledge, news and informal exchanges. But if, a time ago, official authorities controlled the quality of information (i.e. news agencies, universities, writers..), in our time it is more difficult to have confidence on what is pushed and forwarded. This studies is based on datasets from Twitter platform and investigates how the content of text messages is disseminated as a rumour.

Rumour is an old social phenomenon used in politics and other public spaces. It has been studied for only hundred years by sociologists and psychologists by qualitative means. Social media platforms open new opportunities to improve quantitative analyses. We scanned all scientific literature to find relevant features. We made a quantitative screening of some specific rumours (in French and in English). Firstly, we identified some sources of information to find them. Secondly, we compiled different reference, rumouring and event datasets. Thirdly, we considered two facets of a rumour: the way it can spread to other users, and the syntagmatic content that may or may not be specific for a rumour. We found 53 features, clustered into six categories, which are able to describe a rumour message. The spread of a rumour is multi-harmonic having different frequencies and spikes, and can survive several years. Combinations of words (n-grams and skip-grams) are not typical of expressivity between rumours and news but study of lexical transition from a time period to the next goes in the sense of transmission pattern as described by Allport theory of transmission. A rumour can be interpreted as a speech act but with transmission patterns.

About the speaker:

Dr. Nicolas hold a PhD in computer science from university of Strasbourg. In Eastern Paris University (University Paris Est), he has been working more than 10 years as full time research fellow in the area of natural language processing and machine learning. He managed many projects in text data science with application in bioinformatics, environment, and social data analysis. He has written two books recently:

Text Data Science with R, ISTE, 2016

Knowledge Needs and Information Extraction », ISTE-Wiley, 2013