Knowledge Enabled Location Prediction of Twitter Users

As the popularity of online social networking sites such as Twitter and Facebook continues to rise, the volume of textual content generated on the web is increasing rapidly. The mining of user generated content in social media has proven effective in domains ranging from personalization and recommendation systems to crisis management. These applications stand to be further enhanced by incorporating information about the geo-position of social media users in their analysis. Due to privacy concerns, users are largely reluctant to share their location information. As a consequence of this, researchers have focused on automatic inferencing of location information from the contents of a user\u27s tweets. Existing approaches are purely data-driven and require large training data sets of geotagged tweets. Furthermore, these approaches rely solely on social media features or probabilistic language models and fail to capture the underlying semantics of the tweets. In this thesis, we propose a novel knowledge based approach that does not require any training data. Our approach uses Wikipedia, a crowd sourced knowledge base, to extract entities that are relevant to a location. We refer to these entities as local entities. Additionally, we score the relevance of each local entity with respect to the city, using the Wikipedia Hyperlink Graph. We predict the most likely location of the user by matching the scored entities of a city and the entities mentioned by users in their tweets. We evaluate our approach on a publicly available data set consisting of 5119 Twitter users across continental United States and show comparable accuracy to the state-of-the-art approaches. Our results demonstrate the ability to pinpoint the location of a Twitter user to a state and a city using Wikipedia, without needing to train a probabilistic model

Location Prediction of Twitter Users using Wikipedia

The mining of user generated content in social media has proven very effective in domains ranging from personalization and recommendation systems to crisis management. The knowledge of online users locations makes their tweets more informative and adds another dimension to their analysis. Existing approaches to predict the location of Twitter users are purely data-driven and require large training data sets of geo-tagged tweets. The collection and modelling process of tweets can be time intensive. To overcome this drawback, we propose a novel knowledge based approach that does not require any training data. Our approach uses information in Wikipedia, about cities in the geographical area of our interest, to score entities most relevant to a city. By semantically matching the scored entities of a city and the entities mentioned by the user in his/her tweets, we predict the most likely location of the user. Using a publicly available benchmark dataset, we achieve 3% increase in accuracy and 80 miles drop in the average error distance with respect to the state-of-the-art approaches

Quit attempts among tobacco users identified in the Tamil Nadu Tobacco Survey of 2015/2016: a 3 year follow-up mixed methods study

Objectives To determine current tobacco use in 2018/2019, quit attempts made and to explore the enablers and barriers in quitting tobacco among tobacco users identified in the Tamil Nadu Tobacco Survey (TNTS) in 2015/2016.Setting TNTS was conducted in 2015/2016 throughout the state of Tamil Nadu (TN) in India covering 111 363 individuals. Tobacco prevalence was found to be 5.2% (n=5208).Participants All tobacco users in 11 districts of TN identified by TNTS (n=2909) were tracked after 3 years by telephone. In-depth interviews (n=26) were conducted in a subsample to understand the enablers and barriers in quitting.Primary and secondary outcomes Current tobacco use status, any quit attempt and successful quit rate were the primary outcomes, while barriers and enablers in quitting were considered as secondary outcomes.Results Among the 2909 tobacco users identified in TNTS 2015/2016, only 724 (24.9%) could be contacted by telephone, of which 555 (76.7%) consented. Of those who consented, 210 (37.8%) were currently not using tobacco (ie, successfully quit) and 337 (60.7%) continued to use any form of tobacco. Of current tobacco users, 115 (34.1%) have never made any attempt to quit and 193 (57.3.8%) have made an attempt to quit. Those using smoking form of tobacco products (adjusted relative risk (aRR)=1.2, 95% CI: 1.1 to 1.4) and exposure to smoke at home (aRR=1.2, 95% CI: 1.1 to 1.3) were found to be positively associated with continued tobacco use (failed or no quit attempt). Support from family and perceived health benefits are key enablers, while peer influence, high dependence and lack of professional help are some of the barriers to quitting.Conclusion Two-thirds of the tobacco users continue to use tobacco in the last 3 years. While tobacco users are well aware of the ill-effects of tobacco, various intrinsic and extrinsic factors play a major role as a facilitator and lack of the same act as a barrier to quit