GitHub - HunterHeidy/HASOC2020: The large fraction of hate speech and other offensive and objectionable content online poses a huge challenge to societies. Offensive language such as insulting, hurtful, derogatory or obscene content directed from one person to another person and open for others undermines objective discussions. Such type of language can be more increasingly found on the web and can lead to the radicalization of debates. Public opinion forming requires rational critical discourse (Habermas 1984). Objectionable content can pose a threat to democracy. At the same time, open societies need to find an adequate way to react to such content without imposing rigid censorship regimes. As a consequence, many platforms of social media websites monitor user posts. This leads to a pressing demand for methods to automatically identify suspicious posts. Online communities, social media enterprises and technology companies have been investing heavily in technology and processes to identify offensive language in order to prevent abusive behavior in social media. HASOC provides a forum and a data challenge for multilingual research on the identification of problematic content. This year, we offer again 2 sub-tasks for each language such as English, German and Hindi, alltogether over 10.000 annotated tweets from Twitter. Participants in this year’s shared task can choose to participate in one or two of the subtasks. Participants can look at the openly available data of HASOC 2019: https://hasocfire.github.io/hasoc/2019/dataset.html Tasks There are two sub-tasks in each of the languages. Below is a brief description of each task. Sub-task A: Identifying Hate, offensive and profane content This task focus on Hate speech and Offensive language identification offered for English, German, and Hindi. Sub-task A is coarse-grained binary classification in which participating system are required to classify tweets into two classes, namely: Hate and Offensive (HOF) and Non- Hate and offensive (NOT). (NOT) Non Hate-Offensive - This post does not contain any Hate speech, profane, offensive content. (HOF) Hate and Offensive - This post contains Hate, offensive, and profane content. Sub-task B: Discrimination between Hate, profane and offensive posts This sub-task is a fine-grained classification offered for English, German, and Hindi. Hate-speech and offensive posts from the sub-task A are further classified into three categories: (HATE) Hate speech:- Posts under this class contain Hate speech content. (OFFN) Offenive:- Posts under this class contain offensive content. (PRFN) Profane:- These posts contain profane words. Categories Explanation: HATE SPEECH: Describing negative attributes or deficiencies to groups of individuals because they are members of a group (e.g. all poor people are stupid). Hateful comment toward groups because of race, political opinion, sexual orientation, gender, social status, health condition or similar. OFFENSIVE: Posts which are degrading, dehumanizing,insulting an individual,threatening with violent acts are categorized into OFFENSIVE category. PROFANITY: Unacceptable language in the absence of insults and abuse. This typically concerns the usage of swearwords (Scheiße, Fuck etc.) and cursing (Zur Hölle! Verdammt! etc.) are categorized into this category.

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The large fraction of hate speech and other offensive and objectionable content online poses a huge challenge to societies. Offensive language such as insulting, hurtful, derogatory or obscene content directed from one person to another person and open for others undermines objective discussions. Such type of language can be more increasingly fo…

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Name		Name	Last commit message	Last commit date
Latest commit History 1 Commit
data		data
AllFunction.py		AllFunction.py
Hasoc.py		Hasoc.py
Readme.txt		Readme.txt
gm.py		gm.py
propossess.py		propossess.py
submission_de_task1.csv		submission_de_task1.csv
submission_de_task2.csv		submission_de_task2.csv
submission_en_task1.csv		submission_en_task1.csv
submission_en_task2.csv		submission_en_task2.csv

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keras ==2.24
tensorflow==1.14
python==3.6


glove.840B.300d.txt:
http://nlp.stanford.edu/data/wordvecs/glove.840B.300d.zip
fasttext-genman:
https://dl.fbaipublicfiles.com/fasttext/vectors-crawl/cc.de.300.vec.gz