Learning from Social Media Texts

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Applications in the field of Natural Language Processing (NLP) and Machine Learning (ML) have become popular in recent years. This is due to the availability of data and evaluation benchmarks, to progress in the algorithms, and finally to an increased need for these applications in our daily life and in commercial product development. ******I propose to apply NLP and ML techniques for user modelling in social media. There are three specific objectives: (1) Learn user characteristics from social media texts. These characteristics may include: age, gender, personality type, location, ethnicity, health issues, interests, and life events. (2) Learn population distributions in various social media (e.g., Twitter, forums, Facebook) for each of these characteristics. (3) Use the extracted information, as a proof of concept, in applications such as marketing research and health monitoring. ******The scientific approach that I propose is based on machine learning, deep learning, automatic text classification, and information extraction techniques. We will focus our attention on social media texts, which are more challenging than regular texts due to non-standard spelling, lack of editing, abbreviations, jargon, and noise, The techniques need to be adapted to this kind of text. The various ways of adapting them include retraining, partial normalization of the messages, and adding features specific to each type of social media. In addition, I propose to integrate techniques that exploit the structure of the social network. Most of the previous work on related topics uses either the texts of the messages or the network structure. I believe that combining them may lead to an increase in the precision of the extracted information. We will also pay special attention to protecting the privacy of social media users. ******The novelty of the proposed work consists of a comprehensive investigation of the existing techniques, in increasing their sophistication and in developing new techniques for the proposed tasks (particularly at population level, a less-studied area), as well as in the development of several proof-of-concept applications that require information about users or about populations of users in social media. ******We anticipate that the outcomes of the research will contribute to better understanding of human communication in social media, which will allow advances in mining the social media for market research purposes and other decision-making applications, and will strengthen Canada's position as a major player in the field of information technology.