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        <title>Identification of Adverse Drug Reaction Mentions in Tweets – SMM4H Shared Task 2019</title>
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        <namePart type="given">Samarth</namePart>
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        <namePart type="given">Chitta</namePart>
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            <title>Proceedings of the Fourth Social Media Mining for Health Applications (#SMM4H) Workshop &amp; Shared Task</title>
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            <namePart type="given">Davy</namePart>
            <namePart type="family">Weissenbacher</namePart>
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            <namePart type="given">Graciela</namePart>
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    <abstract>Analyzing social media posts can offer insights into a wide range of topics that are commonly discussed online, providing valuable information for studying various health-related phenomena reported online. The outcome of this work can offer insights into pharmacovigilance research to monitor the adverse effects of medications. This research specifically looks into mentions of adverse drug reactions (ADRs) in Twitter data through the Social Media Mining for Health Applications (SMM4H) Shared Task 2019. Adverse drug reactions are undesired harmful effects which can arise from medication or other methods of treatment. The goal of this research is to build accurate models using natural language processing techniques to detect reports of adverse drug reactions in Twitter data and extract these words or phrases.</abstract>
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        <date>2019-08</date>
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%0 Conference Proceedings
%T Identification of Adverse Drug Reaction Mentions in Tweets – SMM4H Shared Task 2019
%A Rawal, Samarth
%A Rawal, Siddharth
%A Anwar, Saadat
%A Baral, Chitta
%Y Weissenbacher, Davy
%Y Gonzalez-Hernandez, Graciela
%S Proceedings of the Fourth Social Media Mining for Health Applications (#SMM4H) Workshop & Shared Task
%D 2019
%8 August
%I Association for Computational Linguistics
%C Florence, Italy
%F rawal-etal-2019-identification
%X Analyzing social media posts can offer insights into a wide range of topics that are commonly discussed online, providing valuable information for studying various health-related phenomena reported online. The outcome of this work can offer insights into pharmacovigilance research to monitor the adverse effects of medications. This research specifically looks into mentions of adverse drug reactions (ADRs) in Twitter data through the Social Media Mining for Health Applications (SMM4H) Shared Task 2019. Adverse drug reactions are undesired harmful effects which can arise from medication or other methods of treatment. The goal of this research is to build accurate models using natural language processing techniques to detect reports of adverse drug reactions in Twitter data and extract these words or phrases.
%R 10.18653/v1/W19-3225
%U https://aclanthology.org/W19-3225
%U https://doi.org/10.18653/v1/W19-3225
%P 136-137

Markdown (Informal)

[Identification of Adverse Drug Reaction Mentions in Tweets – SMM4H Shared Task 2019](https://aclanthology.org/W19-3225) (Rawal et al., ACL 2019)

• Identification of Adverse Drug Reaction Mentions in Tweets – SMM4H Shared Task 2019 (Rawal et al., ACL 2019)

ACL

• Samarth Rawal, Siddharth Rawal, Saadat Anwar, and Chitta Baral. 2019. Identification of Adverse Drug Reaction Mentions in Tweets – SMM4H Shared Task 2019. In Proceedings of the Fourth Social Media Mining for Health Applications (#SMM4H) Workshop & Shared Task, pages 136–137, Florence, Italy. Association for Computational Linguistics.