@inproceedings{saroj-pal-2020-indian,
title = "An {I}ndian Language Social Media Collection for Hate and Offensive Speech",
author = "Saroj, Anita and
Pal, Sukomal",
editor = "Monti, Johanna and
Basile, Valerio and
Buono, Maria Pia Di and
Manna, Raffaele and
Pascucci, Antonio and
Tonelli, Sara",
booktitle = "Proceedings of the Workshop on Resources and Techniques for User and Author Profiling in Abusive Language",
month = may,
year = "2020",
address = "Marseille, France",
publisher = "European Language Resources Association (ELRA)",
url = "https://aclanthology.org/2020.restup-1.2/",
pages = "2--8",
language = "eng",
ISBN = "979-10-95546-49-8",
abstract = "In social media, people express themselves every day on issues that affect their lives. During the parliamentary elections, people`s interaction with the candidates in social media posts reflects a lot of social trends in a charged atmosphere. People`s likes and dislikes on leaders, political parties and their stands often become subject of hate and offensive posts. We collected social media posts in Hindi and English from Facebook and Twitter during the run-up to the parliamentary election 2019 of India (PEI data-2019). We created a dataset for sentiment analysis into three categories: hate speech, offensive and not hate, or not offensive. We report here the initial results of sentiment classification for the dataset using different classifiers."
}
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<abstract>In social media, people express themselves every day on issues that affect their lives. During the parliamentary elections, people‘s interaction with the candidates in social media posts reflects a lot of social trends in a charged atmosphere. People‘s likes and dislikes on leaders, political parties and their stands often become subject of hate and offensive posts. We collected social media posts in Hindi and English from Facebook and Twitter during the run-up to the parliamentary election 2019 of India (PEI data-2019). We created a dataset for sentiment analysis into three categories: hate speech, offensive and not hate, or not offensive. We report here the initial results of sentiment classification for the dataset using different classifiers.</abstract>
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%0 Conference Proceedings
%T An Indian Language Social Media Collection for Hate and Offensive Speech
%A Saroj, Anita
%A Pal, Sukomal
%Y Monti, Johanna
%Y Basile, Valerio
%Y Buono, Maria Pia Di
%Y Manna, Raffaele
%Y Pascucci, Antonio
%Y Tonelli, Sara
%S Proceedings of the Workshop on Resources and Techniques for User and Author Profiling in Abusive Language
%D 2020
%8 May
%I European Language Resources Association (ELRA)
%C Marseille, France
%@ 979-10-95546-49-8
%G eng
%F saroj-pal-2020-indian
%X In social media, people express themselves every day on issues that affect their lives. During the parliamentary elections, people‘s interaction with the candidates in social media posts reflects a lot of social trends in a charged atmosphere. People‘s likes and dislikes on leaders, political parties and their stands often become subject of hate and offensive posts. We collected social media posts in Hindi and English from Facebook and Twitter during the run-up to the parliamentary election 2019 of India (PEI data-2019). We created a dataset for sentiment analysis into three categories: hate speech, offensive and not hate, or not offensive. We report here the initial results of sentiment classification for the dataset using different classifiers.
%U https://aclanthology.org/2020.restup-1.2/
%P 2-8
Markdown (Informal)
[An Indian Language Social Media Collection for Hate and Offensive Speech](https://aclanthology.org/2020.restup-1.2/) (Saroj & Pal, ResTUP 2020)
ACL