Pairing Tweets with the Right Location

Twitter is used to provide location-relevant information and event updates. It is important to identify location-relevant tweets in order to harness location-relevant information and event updates from Twitter. However, the identification of location-relevant tweets is a challenging problem as the location names are not always explicit. Instead, mostly the location names are implicitly embedded in tweets. This research proposes a novel approach, labelled as DigiCities, to add geographical context to non-geo tagged tweets. The proposed approach helps in improving identification of location-relevant tweet by harnessing the location-specific information embedded in user-ids and hashtags included in tweets. Tweets relevant to eight cities were identified and used in classification experiments, and the use of DigiCities improved the overall classification accuracy of tweets into relevant city classes.

[1]  Harith Alani,et al.  Semantic Sentiment Analysis of Twitter , 2012, SEMWEB.

[2]  Gisele L. Pappa,et al.  Inferring the Location of Twitter Messages Based on User Relationships , 2011, Trans. GIS.

[3]  Michele Risi,et al.  Automatic Event Geo-Location in Twitter , 2020, IEEE Access.

[4]  H. Kraemer,et al.  The role and interpretation of pilot studies in clinical research. , 2011, Journal of psychiatric research.

[5]  Yogesh Kumar Dwivedi,et al.  Event classification and location prediction from tweets during disasters , 2017, Annals of Operations Research.

[6]  Abhinav Kumar,et al.  Location reference identification from tweets during emergencies: A deep learning approach , 2019, International Journal of Disaster Risk Reduction.

[7]  Bill Serra,et al.  People, Places, Things: Web Presence for the Real World , 2002, Mob. Networks Appl..

[8]  Onur Varol,et al.  What is gained and what is left to be done when content analysis is added to network analysis in the study of a social movement: Twitter use during Gezi Park , 2017 .

[9]  C. Teddlie,et al.  Mixed Methods Sampling , 2007 .

[10]  Serkan Günal,et al.  The impact of preprocessing on text classification , 2014, Inf. Process. Manag..

[11]  Lukasz Kaiser,et al.  Attention is All you Need , 2017, NIPS.

[12]  Dongwon Lee,et al.  @Phillies Tweeting from Philly? Predicting Twitter User Locations with Spatial Word Usage , 2012, 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining.

[13]  Paulo Cortez,et al.  A Google Trends spatial clustering approach for a worldwide Twitter user geolocation , 2020, Inf. Process. Manag..

[14]  Philippe Thomas,et al.  Twitter Geolocation Prediction Using Neural Networks , 2017, GSCL.

[15]  Aixin Sun,et al.  A Survey of Location Prediction on Twitter , 2017, IEEE Transactions on Knowledge and Data Engineering.

[16]  P. Biernacki,et al.  Snowball Sampling: Problems and Techniques of Chain Referral Sampling , 1981 .

[17]  Daniel Z. Sui,et al.  From GIS to neogeography: ontological implications and theories of truth , 2010, Ann. GIS.

[18]  Yang Li,et al.  Inferring event geolocation based on Twitter , 2018, ICIMCS '18.

[19]  Rui Li,et al.  Multiple Location Profiling for Users and Relationships from Social Network and Content , 2012, Proc. VLDB Endow..

[20]  Jingrui He,et al.  Location Prediction for Tweets , 2019, Front. Big Data.

[21]  Hyunjin Park,et al.  Classification of the glioma grading using radiomics analysis , 2018, PeerJ.

[22]  Alexander J. Smola,et al.  Discovering geographical topics in the twitter stream , 2012, WWW.

[23]  Mudhakar Srivatsa,et al.  When twitter meets foursquare: tweet location prediction using foursquare , 2014, MobiQuitous.

[24]  Scott A. Hale,et al.  Where in the World Are You? Geolocation and Language Identification in Twitter* , 2013, ArXiv.

[25]  Chih-Jen Lin,et al.  A Practical Guide to Support Vector Classication , 2008 .

[26]  Ingrid Rogstad,et al.  Is Twitter just rehashing? Intermedia agenda setting between Twitter and mainstream media , 2016 .

[27]  Wei Shen,et al.  Predicting Named Entity Location Using Twitter , 2018, 2018 IEEE 34th International Conference on Data Engineering (ICDE).

[28]  Muhammad Imran,et al.  Automatic identification of eyewitness messages on twitter during disasters , 2020, Inf. Process. Manag..

[29]  Oguz Akbilgic,et al.  Improved detection of congestive heart failure via probabilistic symbolic pattern recognition and heart rate variability metrics , 2017, Int. J. Medical Informatics.

[30]  Sharon Myrtle Paradesi,et al.  Geotagging Tweets Using Their Content , 2011, FLAIRS.

[31]  Xiangyang Luo,et al.  Twitter User Location Inference Based on Representation Learning and Label Propagation , 2020, WWW.

[32]  Kyumin Lee,et al.  You are where you tweet: a content-based approach to geo-locating twitter users , 2010, CIKM.

[33]  Diana Inkpen,et al.  Detecting and Disambiguating Locations Mentioned in Twitter Messages , 2015, CICLing.

[34]  Ahmed K. Jameil,et al.  A novel algorithm for estimation of Twitter users location using public available information , 2020, International Journal on Smart Sensing and Intelligent Systems.

[35]  Kazufumi Watanabe,et al.  Jasmine: a real-time local-event detection system based on geolocation information propagated to microblogs , 2011, CIKM '11.

[36]  Jordán Pascual Espada,et al.  Machine learning approach for text and document mining , 2014, ArXiv.

[37]  Thorsten Joachims,et al.  Making large scale SVM learning practical , 1998 .

[38]  James Caverlee,et al.  Location prediction in social media based on tie strength , 2013, CIKM.

[39]  Charles Teddlie,et al.  Mixed Methods Sampling A Typology With Examples , 2016 .

[40]  Philip M. Massey,et al.  Applying Multiple Data Collection Tools to Quantify Human Papillomavirus Vaccine Communication on Twitter , 2016, Journal of medical Internet research.