Identification of Emergency Blood Donation Request on Twitter

Social media-based text mining in healthcare has received special attention in recent times due to the enhanced accessibility of social media sites like Twitter. The increasing trend of spreading important information in distress can help patients reach out to prospective blood donors in a time bound manner. However such manual efforts are mostly inefficient due to the limited network of a user. In a novel step to solve this problem, we present an annotated Emergency Blood Donation Request (EBDR) dataset to classify tweets referring to the necessity of urgent blood donation requirement. Additionally, we also present an automated feature-based SVM classification technique that can help selective EBDR tweets reach relevant personals as well as medical authorities. Our experiments also present a quantitative evidence that linguistic along with handcrafted heuristics can act as the most representative set of signals this task with an accuracy of 97.89%.

[1]  Juan Enrique Ramos,et al.  Using TF-IDF to Determine Word Relevance in Document Queries , 2003 .

[2]  Michael J. Paul,et al.  Using Social Media to Perform Local Influenza Surveillance in an Inner-City Hospital: A Retrospective Observational Study , 2015, JMIR public health and surveillance.

[3]  Ramit Sawhney,et al.  Did you offend me? Classification of Offensive Tweets in Hinglish Language , 2018, ALW.

[4]  Christophe Giraud-Carrier,et al.  An Explor ation of Social Circles and Prescr iption Drug Ab use Through Twitter , 2013 .

[5]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[6]  Raquel Recuero,et al.  How Does Social Capital Affect Retweets? , 2011, ICWSM.

[7]  Rajiv Ratn Shah,et al.  Did you take the pill? - Detecting Personal Intake of Medicine from Twitter , 2018, ArXiv.

[8]  Ravi Shankar,et al.  A Comparative Study of Transfer Functions in Binary Evolutionary Algorithms for Single Objective Optimization , 2018, DCAI.

[9]  Ravi Shankar,et al.  A Firefly Algorithm Based Wrapper-Penalty Feature Selection Method for Cancer Diagnosis , 2018, ICCSA.

[10]  Krishna P. Gummadi,et al.  Measuring User Influence in Twitter: The Million Follower Fallacy , 2010, ICWSM.

[11]  Ramit Sawhney,et al.  Detecting Offensive Tweets in Hindi-English Code-Switched Language , 2018, SocialNLP@ACL.

[12]  Swati Aggarwal,et al.  A Computational Approach to Feature Extraction for Identification of Suicidal Ideation in Tweets , 2018, ACL.

[13]  Paul A. Fontelo,et al.  Development of an Adverse Drug Reaction Corpus from Consumer Health Posts for Psychiatric Medications , 2017, SMM4H@AMIA.