text
stringlengths
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class label
6 classes
Tik tok alli jagala madtidralla adra baggenu ondu video madi anna super agi ugididdira
0Not_offensive
Anyone from kerala here
5not-Kannada
Movie rerelease madi plss
0Not_offensive
Amazon prime alli bittidira....yella manele nodtare....
0Not_offensive
Guru sure news nanu tik tok dawn lod madeda yavaga nama nDudu tindu nama dashda mala uddha madaka bandro avaga deleted
0Not_offensive
ಸುದೀಪ್ ಸರ್ ಅಂಡ್ ದರ್ಶನ್ ಸರ್ ಅವರಿಗೆ ಇರೋ ಫ್ಯಾನ್ಸ್ ಫಾಲೋಯಿಂಗ್ ರಕ್ಷಿತ್ ಶೆಟ್ಟಿ ಇಲ್ಲ ಅಂದ್ರು ಸಾಂಗ್ 2m ಮತ್ತು ಟೀಸರ್ 8.5.m ಬೆಂಕಿ ಗುರು ಮೂವಿ ವೇಟಿಂಗ್
0Not_offensive
Ade old same story
3Offensive_Targeted_Insult_Group
Superb rakshit sir sobg superb macking
5not-Kannada
Hai Neel (prithvi) wonderful movie dia
5not-Kannada
ಏನ್ ಗುರು ನಿನ್ನ ಚರಿತ್ರೆ ಸೃಷ್ಟಿಸುವ ಅವತಾರ ತುಂಬಾ ಚೆನ್ನಾಗಿ ಇದೆ ರಶ್ಮಿಕಾಗೆ ತುಂಬಾ ಉರುಸು
0Not_offensive
E tara film ge ella kaitaididdu.... Welcooome..... Item song iro
0Not_offensive
Super guru 🥰
0Not_offensive
Different aagide song... n
0Not_offensive
D BOOS FANS ALL THE BEST
5not-Kannada
ನಿಜವಾಗಿಯೂ ಅದ್ಭುತ heartly heltidini... plz avrigella namma nimmellara supprt beku
0Not_offensive
I'm speechless
0Not_offensive
Movie was amazing!!🤩🤩
5not-Kannada
BLACK HEART ala
0Not_offensive
ಬ್ರೋ ಅವರು ಗೆ ದೇಶದ ಬಗ್ಗೆ ಅಭಿಮಾನ ಇಲ್ಲಾ ಬಿಡಿ
4Offensive_Targeted_Insult_Other
ಹೆಮ್ಮೆಯ ಕನ್ನಡಿಗ
0Not_offensive
ಇನ್ನೊಮ್ಮೆ ಬಿಡುಗಡೆ ಮಾಡಿ ಗೆಲ್ಸೋ ಜವಾಬ್ದಾರಿ ನಮ್ಮದು....#ಮತ್ತೊಮ್ಮೆ ದಿಯಾ.
0Not_offensive
Movie supper sir
5not-Kannada
Super sir keep it
5not-Kannada
ಇನ್ನು ಕೇವಲ 1000 ಮೆಚ್ಚುಗೆಗಳು(ಲೈಕ್) ಬೇಕಿದೆ.. ಅತಿ ಹೆಚ್ಚು ಮೆಚ್ಚುಗೆಗಳು(ಲೈಕ್) ಪಡೆದ ಕನ್ನಡ ಚಲನಚಿತ್ರದ ದೃಶ್ಯ ಗೀತೆ (ವೀಡಿಯೊ ಹಾಡು) ಆಗಲು nಯೂಟ್ಯೂಬ್ ನಲ್ಲಿ.. nದಿನಾಂಕ
0Not_offensive
ಶ್ರೀ ಮನ್ ನಾರಾಯಣ movie ಹಾಡು ಕೇಳಲು ಸುಮಧುರ...... ಡ್ಯಾನ್ಸ್ ಸೂಪರ್.... ಇದೆ
0Not_offensive
ದೇಶಧ್ರೋಹಿಗಳು ಡಿಸ್ ಲೈಕ್ ಮಾಡಿದರೆ ನೀವು ನಿಜವಾದ ದೇಶ ದ್ರೋಹಿಗಳು ನಾಚಿಕೆ ಆಗಬೇಕು ನಿಮ್ಮ ಜನ್ಮಕ್ಕೆ.... ಇಂತಹ ಅದ್ಭುತವಾದ ವಿಡಿಯೋ ಗಳಿಗೂ ಡಿಸ್ ಲೈಕ್ ಮಾಡಿದ್ರಲ್ಲ ನಿಮಗೆ ನಾಚಿಕೆ ಆಗಲ್ವಾ.....
3Offensive_Targeted_Insult_Group
Randhawa movie 10 rating ide
0Not_offensive
ಸೂಪರ್ ಬಗ್ರೌಂಡ್ music
0Not_offensive
Eno ontara chnnagide annoru like madi🤔🤔🤔🤔🤔🤔🤔🤔🤔
0Not_offensive
Bindu gowda ge super agi ugididdeera
2Offensive_Targeted_Insult_Individual
Kannadadalli mado badlu hindinalli madidre superrhit agodhu
1Offensive_Untargetede
Rip dislikers
5not-Kannada
Namana Kudlada rakshith shetty innu orsbeku rashmikage Thu avla Janmake
2Offensive_Targeted_Insult_Individual
ಅದ್ಬುತ ಕಲೆ ಸಾಹಿತ್ಯ ಸಂಗೀತ ಗಾಯನ
0Not_offensive
Yes boss e film maatea 7 sala alla 10 sala nodidru bore aagalla
0Not_offensive
@Troll Stupid Fans heno helkodthidallla adunna helkodo munche neenu kaltho henge irbeku antha suvar gandu anthidde ivga ninge baididke thika huritha
2Offensive_Targeted_Insult_Individual
tippa yappa supper
0Not_offensive
Yar guru ninu.....super
0Not_offensive
@prithvi nayak nimma Amman tulla suledeepa movie yavdu hit aagide helu recent aagi bandidralli
2Offensive_Targeted_Insult_Individual
Really nyc sakath chenagi madidira nange esta aythu. Super riii adrallu tiktok dattu putra nna sakath roast madi heltini...
0Not_offensive
ಪ್ರೀತಿಗೋಸ್ಕರ ಪ್ರಾಣನೆ ಬಿಡ್ತಾರೆ
4Offensive_Targeted_Insult_Other
Thank you so much
5not-Kannada
ದೃಷ್ಟಿ ನನ್ನೋಬ್ಬನ ಮೇಲಿಡಿ ತಪ್ಪದು ನಿಜ ಮನರಂಜನೆ film nodo munchene ee song ista adavaru like madi
0Not_offensive
Indian no.1 bgm
5not-Kannada
ಮೆಲ್ಲನೆ ನಶೆ ಎರಿಸೊ ಹಾಡು
0Not_offensive
Nimm avvan tull ...kannag en tunni itkond nodi en Randi magagna
3Offensive_Targeted_Insult_Group
Nice story accting super prajval devraaj
0Not_offensive
Bro sorry ond matu nang eetara troll madodu ella ishta agtirlilla first time nimm videos nodi sakkath kushiaytu nd ur video really give a good msg
0Not_offensive
hindi m bhi daal. do yaaro
5not-Kannada
love you shankar sir ........namage neevu beku sir !!! matte neevu nammanna manaranjane maadi neevu illade iro kannada cinema naanu nodalla sir .....
0Not_offensive
SUPER R BOSS
0Not_offensive
ಸಾಹಿತ್ಯ ಓದಿ ಹಾಡು ಕೇಳಿ ಆನಂದಿಸಿ.
0Not_offensive
ಸೂಪರ್ ಗುರು ನಿಜ
0Not_offensive
All industries are going hands up.. 27kke avanu bandaga
0Not_offensive
Hoooo super guru nin voice bhal superrrr
0Not_offensive
Delete hadru nanu downloade made
0Not_offensive
ಹಾ ದೇವರು ಒಳ್ಳೆಯವರಿಗೇ ಯಾವಾಗಲೂ ಕಷ್ಟ ಕೋಡದು ಸರ್ ನಿಮಗೇ ಒಳ್ಳೆದಾಗಲ್ಲಿ... ಸರ್..
0Not_offensive
ನಮ್ಮ ಕಾಮೆಂಟ್ ನೋಡಿ ಓದಿದವರ ಸಮಸ್ಯೆಗಳು ಬೇಗ ಪರಿಹಾರವಾಗಲಿ...ಓಂ ನಮೋ ನಾರಯಣಯ ನಮಃ
0Not_offensive
ನೈಸ್ ಚಿತ್ರ ಅವನೇ ಶ್ರೀ ಮಾನ್ ನಾರಾಯಣ........ ಸೂಪರ್ ರಕ್ಷಿತ್ ಶೆಟ್ಟಿ.......
0Not_offensive
ಚಂದನ್ ಶೆಟ್ಟಿ ಟ್ರೊಲ್ ವಿಡಿಯೋ ನೀವು ನೋಡಿ ನೆಗಡ್ಡೆ ಇದ್ರೆ ನಾವ್ ಗ್ಯಾರಂಟಿ
0Not_offensive
Malayalis pore
5not-Kannada
Sir ನಮಸ್ಕಾರ ಕಲಿಯುಗದಲ್ಲಿ ಅಶ್ವತ್ಥಾಮ ಇದ್ದಾರೆ ಅಂತ ಹೇಳಿದರು .ಎಲ್ಲಿ ಇದ್ದಾರೆ ಅವರು ನಾವು ನೋಡಬಹುದ ನನು ನೊಡಬೇಕು ಅಂತಾ ಇದೇ ಅಗತ್ತ sir
0Not_offensive
I'm full happy bro 🤣🤣
5not-Kannada
Rakshit shetty superb
0Not_offensive
Darshan Sir swalpa sahaya madidare
0Not_offensive
ಮಸ್ತ್ ಇದೆ ಈ ಹಾಡು
0Not_offensive
Super sir namge thumba istavada kathe nam parents ge kelisbeku aadastu bega idanna
0Not_offensive
@RAM jnh MNR ss
5not-Kannada
Barri dabba movie ne unn maado du
3Offensive_Targeted_Insult_Group
ಪಕ್ಕಾ ಇದು ಚರಿತ್ರೆ ಸೃಷ್ಠಿ ಸೋ ಅವತಾರ
0Not_offensive
Sir naanu Visakhapatnam li iradhu
0Not_offensive
Yar muje ye movie hindi me dekhni hai
5not-Kannada
real life great sir
0Not_offensive
@Manju Sonu ಅಭಿನಂದನೆ
0Not_offensive
Bro hindi language pls
5not-Kannada
Ha sanchari vijay waiting to see him
5not-Kannada
ಆವಾಗಾವಾಗ ಈ ಹಾಡು ಕೇಳಲಿಲ್ಲ ಅಂದ್ರೆ ಏನೋ ಮಿಸ್ ಮಾಡಿದಾಹಾಗೆ ಆಗುತ್ತೆnSlow poison
0Not_offensive
Inspired from korean movie Midnight Runners
0Not_offensive
ಚಂದನ್ ಶೆಟ್ಟಿ ಟ್ರೊಲ್ ವಿಡಿಯೋ ನೀವು ನೋಡಿ ನೆಗಡ್ಡೆ ಇದ್ರೆ ನಾವ್ ಗ್ಯಾರಂಟಿ
0Not_offensive
gadag our city
5not-Kannada
Awsome movie must match movie
0Not_offensive
Thanks for likes
5not-Kannada
Kannada film industry avaru sir solpa nodi rii yen madta ididra inta ashvath sir avara maga ondu sanni veshe dalli kalsa Kodi
0Not_offensive
Yes Nam ura hudgange mosa Madi hodlu ..amele Nam Karnataka kk avmana madidlu avlige yakadru hode ansbeku
2Offensive_Targeted_Insult_Individual
I hate tiktok mobile thagondagindanu install made illa i hate this
4Offensive_Targeted_Insult_Other
Howdu GurunAdre climax papa
0Not_offensive
ಸಿನಿಮಾ ಬಿಡುಗಡೆಗು ಮುನ್ನ ಹಾಡು ಬಿಡುಗಡೆ ಮಾಡೋಕೆ ಕನ್ನಡ ಇಂಡಸ್ಟ್ರಿ ಇಂದ ಮಾತ್ರ ಸಾಧ್ಯ. # ಅವನೇ ಶ್ರೀ ಮನ್ನಾ ನಾರಾಯಣ.
0Not_offensive
Dis like hakorge ond math hekok esta padthini nimig esta agilla andre sumne erbod alva yak sumne dislike hakthira ... Ade bere bhashed est kacchada edru like hakthira alva nimmanthor erodrindane Kannada movies ast hesru madtha illa ...
3Offensive_Targeted_Insult_Group
Next level quality
5not-Kannada
Le gandunTikamucchunnEe Tara worst song nin kayalli madakke agutta
4Offensive_Targeted_Insult_Other
E song ero range ge 3to.5 millone agbekittu
0Not_offensive
ಚಿತ್ರರಂಗದ ಧನಿಕರು ಎಲ್ಲಿದ್ದಿರ. ನಾಲ್ಕು ಜನ ಸೇರಿದರೆ ಸಾಕು
0Not_offensive
ರಶ್ಮಿಕ ರಕ್ಷಿತ್ ಶೆಟ್ಟಿ ಬೆಳವಣಿಗೆ ನೋಡಿ ?
4Offensive_Targeted_Insult_Other
kelsa maddidare anna Modiji
0Not_offensive
Chalu ati nataka
0Not_offensive
super togari tippa
5not-Kannada
Previous video dalli sai Kumar diolauge
0Not_offensive
Hands up to vijaya prakash sirr
5not-Kannada
En ಕಮೆಂಟ್ ಗುರು ಎಲ್ ಇರ್ತೀರೋ ನೀವೆಲ್ಲಾ ಸೂಪರ್
0Not_offensive
Hands up - 308k likes nBelageddu - 308k likes
4Offensive_Targeted_Insult_Other

Dataset Card for Offenseval Dravidian

Dataset Summary

Offensive language identification is classification task in natural language processing (NLP) where the aim is to moderate and minimise offensive content in social media. It has been an active area of research in both academia and industry for the past two decades. There is an increasing demand for offensive language identification on social media texts which are largely code-mixed. Code-mixing is a prevalent phenomenon in a multilingual community and the code-mixed texts are sometimes written in non-native scripts. Systems trained on monolingual data fail on code-mixed data due to the complexity of code-switching at different linguistic levels in the text. This shared task presents a new gold standard corpus for offensive language identification of code-mixed text in Dravidian languages (Tamil-English, Malayalam-English, and Kannada-English).

Supported Tasks and Leaderboards

The goal of this task is to identify offensive language content of the code-mixed dataset of comments/posts in Dravidian Languages ( (Tamil-English, Malayalam-English, and Kannada-English)) collected from social media. The comment/post may contain more than one sentence but the average sentence length of the corpora is 1. Each comment/post is annotated at the comment/post level. This dataset also has class imbalance problems depicting real-world scenarios.

Languages

Code-mixed text in Dravidian languages (Tamil-English, Malayalam-English, and Kannada-English).

Dataset Structure

Data Instances

An example from the Tamil dataset looks as follows:

text label
படம் கண்டிப்பாக வெற்றி பெற வேண்டும் செம்ம vara level Not_offensive
Avasara patutiya editor uhh antha bullet sequence aa nee soliruka kudathu, athu sollama iruntha movie ku konjam support aa surprise element aa irunthurukum Not_offensive

An example from the Malayalam dataset looks as follows:

text label
ഷൈലോക്ക് ന്റെ നല്ല ടീസർ ആയിട്ട് പോലും ട്രോളി നടന്ന ലാലേട്ടൻ ഫാൻസിന് കിട്ടിയൊരു നല്ലൊരു തിരിച്ചടി തന്നെ ആയിരിന്നു ബിഗ് ബ്രദർ ന്റെ ട്രെയ്‌ലർ Not_offensive
Marana mass Ekka kku kodukku oru Not_offensive

An example from the Kannada dataset looks as follows:

text label
ನಿಜವಾಗಿಯೂ ಅದ್ಭುತ heartly heltidini... plz avrigella namma nimmellara supprt beku Not_offensive
Next song gu kuda alru andre evaga yar comment madidera alla alrru like madi share madi nam industry na next level ge togond hogaona. Not_offensive

Data Fields

Tamil

  • text: Tamil-English code mixed comment.
  • label: integer from 0 to 5 that corresponds to these values: "Not_offensive", "Offensive_Untargetede", "Offensive_Targeted_Insult_Individual", "Offensive_Targeted_Insult_Group", "Offensive_Targeted_Insult_Other", "not-Tamil"

Malayalam

  • text: Malayalam-English code mixed comment.
  • label: integer from 0 to 5 that corresponds to these values: "Not_offensive", "Offensive_Untargetede", "Offensive_Targeted_Insult_Individual", "Offensive_Targeted_Insult_Group", "Offensive_Targeted_Insult_Other", "not-malayalam"

Kannada

  • text: Kannada-English code mixed comment.
  • label: integer from 0 to 5 that corresponds to these values: "Not_offensive", "Offensive_Untargetede", "Offensive_Targeted_Insult_Individual", "Offensive_Targeted_Insult_Group", "Offensive_Targeted_Insult_Other", "not-Kannada"

Data Splits

train validation
Tamil 35139 4388
Malayalam 16010 1999
Kannada 6217 777

Dataset Creation

Curation Rationale

There is an increasing demand for offensive language identification on social media texts which are largely code-mixed. Code-mixing is a prevalent phenomenon in a multilingual community and the code-mixed texts are sometimes written in non-native scripts. Systems trained on monolingual data fail on code-mixed data due to the complexity of code-switching at different linguistic levels in the text.

Source Data

Initial Data Collection and Normalization

[Needs More Information]

Who are the source language producers?

Youtube users

Annotations

Annotation process

[Needs More Information]

Who are the annotators?

[Needs More Information]

Personal and Sensitive Information

[Needs More Information]

Considerations for Using the Data

Social Impact of Dataset

[Needs More Information]

Discussion of Biases

[Needs More Information]

Other Known Limitations

[Needs More Information]

Additional Information

Dataset Curators

[Needs More Information]

Licensing Information

This work is licensed under a Creative Commons Attribution 4.0 International Licence

Citation Information

@article{chakravarthi-etal-2021-lre,
title = "DravidianCodeMix: Sentiment Analysis and Offensive Language Identification Dataset for Dravidian Languages in Code-Mixed Text",
author = "Chakravarthi, Bharathi Raja  and
  Priyadharshini, Ruba  and
  Muralidaran, Vigneshwaran and
  Jose, Navya and
  Suryawanshi, Shardul and
  Sherly, Elizabeth  and
  McCrae, John P",
  journal={Language Resources and Evaluation},
  publisher={Springer}
}
@inproceedings{dravidianoffensive-eacl,
title={Findings of the Shared Task on {O}ffensive {L}anguage {I}dentification in {T}amil, {M}alayalam, and {K}annada},
author={Chakravarthi, Bharathi Raja and
Priyadharshini, Ruba and
Jose, Navya and
M, Anand Kumar and
Mandl, Thomas and
Kumaresan, Prasanna Kumar and
Ponnsamy, Rahul and
V,Hariharan and
Sherly, Elizabeth and
McCrae, John Philip },
booktitle = "Proceedings of the First Workshop on Speech and Language Technologies for Dravidian Languages",
month = April,
year = "2021",
publisher = "Association for Computational Linguistics",
year={2021}
}
@inproceedings{hande-etal-2020-kancmd,
    title = "{K}an{CMD}: {K}annada {C}ode{M}ixed Dataset for Sentiment Analysis and Offensive Language Detection",
    author = "Hande, Adeep  and
      Priyadharshini, Ruba  and
      Chakravarthi, Bharathi Raja",
    booktitle = "Proceedings of the Third Workshop on Computational Modeling of People's Opinions, Personality, and Emotion's in Social Media",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.peoples-1.6",
    pages = "54--63",
    abstract = "We introduce Kannada CodeMixed Dataset (KanCMD), a multi-task learning dataset for sentiment analysis and offensive language identification. The KanCMD dataset highlights two real-world issues from the social media text. First, it contains actual comments in code mixed text posted by users on YouTube social media, rather than in monolingual text from the textbook. Second, it has been annotated for two tasks, namely sentiment analysis and offensive language detection for under-resourced Kannada language. Hence, KanCMD is meant to stimulate research in under-resourced Kannada language on real-world code-mixed social media text and multi-task learning. KanCMD was obtained by crawling the YouTube, and a minimum of three annotators annotates each comment. We release KanCMD 7,671 comments for multitask learning research purpose.",
}
@inproceedings{chakravarthi-etal-2020-corpus,
    title = "Corpus Creation for Sentiment Analysis in Code-Mixed {T}amil-{E}nglish Text",
    author = "Chakravarthi, Bharathi Raja  and
      Muralidaran, Vigneshwaran  and
      Priyadharshini, Ruba  and
      McCrae, John Philip",
    booktitle = "Proceedings of the 1st Joint Workshop on Spoken Language Technologies for Under-resourced languages (SLTU) and Collaboration and Computing for Under-Resourced Languages (CCURL)",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources association",
    url = "https://www.aclweb.org/anthology/2020.sltu-1.28",
    pages = "202--210",
    abstract = "Understanding the sentiment of a comment from a video or an image is an essential task in many applications. Sentiment analysis of a text can be useful for various decision-making processes. One such application is to analyse the popular sentiments of videos on social media based on viewer comments. However, comments from social media do not follow strict rules of grammar, and they contain mixing of more than one language, often written in non-native scripts. Non-availability of annotated code-mixed data for a low-resourced language like Tamil also adds difficulty to this problem. To overcome this, we created a gold standard Tamil-English code-switched, sentiment-annotated corpus containing 15,744 comment posts from YouTube. In this paper, we describe the process of creating the corpus and assigning polarities. We present inter-annotator agreement and show the results of sentiment analysis trained on this corpus as a benchmark.",
    language = "English",
    ISBN = "979-10-95546-35-1",
}
@inproceedings{chakravarthi-etal-2020-sentiment,
    title = "A Sentiment Analysis Dataset for Code-Mixed {M}alayalam-{E}nglish",
    author = "Chakravarthi, Bharathi Raja  and
      Jose, Navya  and
      Suryawanshi, Shardul  and
      Sherly, Elizabeth  and
      McCrae, John Philip",
    booktitle = "Proceedings of the 1st Joint Workshop on Spoken Language Technologies for Under-resourced languages (SLTU) and Collaboration and Computing for Under-Resourced Languages (CCURL)",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources association",
    url = "https://www.aclweb.org/anthology/2020.sltu-1.25",
    pages = "177--184",
    abstract = "There is an increasing demand for sentiment analysis of text from social media which are mostly code-mixed. Systems trained on monolingual data fail for code-mixed data due to the complexity of mixing at different levels of the text. However, very few resources are available for code-mixed data to create models specific for this data. Although much research in multilingual and cross-lingual sentiment analysis has used semi-supervised or unsupervised methods, supervised methods still performs better. Only a few datasets for popular languages such as English-Spanish, English-Hindi, and English-Chinese are available. There are no resources available for Malayalam-English code-mixed data. This paper presents a new gold standard corpus for sentiment analysis of code-mixed text in Malayalam-English annotated by voluntary annotators. This gold standard corpus obtained a Krippendorff{'}s alpha above 0.8 for the dataset. We use this new corpus to provide the benchmark for sentiment analysis in Malayalam-English code-mixed texts.",
    language = "English",
    ISBN = "979-10-95546-35-1",
}

Contributions

Thanks to @jamespaultg for adding this dataset.

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Models trained or fine-tuned on community-datasets/offenseval_dravidian