2020
Safi Samghabadi, Niloofar; López Monroy, Adrián Pastor; Solorio, Thamar
Detecting Early Signs of Cyberbullying in Social Media Inproceedings
In: Proceedings of the Second Workshop on Trolling, Aggression and Cyberbullying, pp. 144–149, European Language Resources Association (ELRA), Marseille, France, 2020, ISBN: 979-10-95546-56-6.
Abstract | Links | BibTeX | Tags: Abusive Language detection
@inproceedings{safi-samghabadi-etal-2020-detecting,
title = {Detecting Early Signs of Cyberbullying in Social Media},
author = {Safi Samghabadi, Niloofar and
López Monroy, Adrián Pastor and
Solorio, Thamar},
url = {https://www.aclweb.org/anthology/2020.trac-1.23},
isbn = {979-10-95546-56-6},
year = {2020},
date = {2020-01-01},
booktitle = {Proceedings of the Second Workshop on Trolling, Aggression and Cyberbullying},
pages = {144--149},
publisher = {European Language Resources Association (ELRA)},
address = {Marseille, France},
abstract = {Nowadays, the amount of users' activities on online social media is growing dramatically. These online environments provide excellent opportunities for communication and knowledge sharing. However, some people misuse them to harass and bully others online, a phenomenon called cyberbullying. Due to its harmful effects on people, especially youth, it is imperative to detect cyberbullying as early as possible before it causes irreparable damages to victims. Most of the relevant available resources are not explicitly designed to detect cyberbullying, but related content, such as hate speech and abusive language. In this paper, we propose a new approach to create a corpus suited for cyberbullying detection. We also investigate the possibility of designing a framework to monitor the streams of users' online messages and detects the signs of cyberbullying as early as possible.},
keywords = {Abusive Language detection},
pubstate = {published},
tppubtype = {inproceedings}
}
Nowadays, the amount of users’ activities on online social media is growing dramatically. These online environments provide excellent opportunities for communication and knowledge sharing. However, some people misuse them to harass and bully others online, a phenomenon called cyberbullying. Due to its harmful effects on people, especially youth, it is imperative to detect cyberbullying as early as possible before it causes irreparable damages to victims. Most of the relevant available resources are not explicitly designed to detect cyberbullying, but related content, such as hate speech and abusive language. In this paper, we propose a new approach to create a corpus suited for cyberbullying detection. We also investigate the possibility of designing a framework to monitor the streams of users’ online messages and detects the signs of cyberbullying as early as possible.
Safi Samghabadi, Niloofar; Patwa, Parth; PYKL, Srinivas; Mukherjee, Prerana; Das, Amitava; Solorio, Thamar
Aggression and Misogyny Detection using BERT: A Multi-Task Approach Inproceedings
In: Proceedings of the Second Workshop on Trolling, Aggression and Cyberbullying, pp. 126–131, European Language Resources Association (ELRA), Marseille, France, 2020, ISBN: 979-10-95546-56-6.
Abstract | Links | BibTeX | Tags: Abusive Language detection
@inproceedings{safi-samghabadi-etal-2020-aggression,
title = {Aggression and Misogyny Detection using BERT: A Multi-Task Approach},
author = {Safi Samghabadi, Niloofar and
Patwa, Parth and
PYKL, Srinivas and
Mukherjee, Prerana and
Das, Amitava and
Solorio, Thamar},
url = {https://www.aclweb.org/anthology/2020.trac-1.20},
isbn = {979-10-95546-56-6},
year = {2020},
date = {2020-01-01},
booktitle = {Proceedings of the Second Workshop on Trolling, Aggression and Cyberbullying},
pages = {126--131},
publisher = {European Language Resources Association (ELRA)},
address = {Marseille, France},
abstract = {In recent times, the focus of the NLP community has increased towards offensive language, aggression, and hate-speech detection.This paper presents our system for TRAC-2 shared task on ``Aggression Identification'' (sub-task A) and ``Misogynistic Aggression Identification'' (sub-task B). The data for this shared task is provided in three different languages - English, Hindi, and Bengali. Each data instance is annotated into one of the three aggression classes - Not Aggressive, Covertly Aggressive, Overtly Aggressive, as well as one of the two misogyny classes - Gendered and Non-Gendered. We propose an end-to-end neural model using attention on top of BERT that incorporates a multi-task learning paradigm to address both the sub-tasks simultaneously. Our team, ``na14'', scored 0.8579 weighted F1-measure on the English sub-task B and secured 3rd rank out of 15 teams for the task. The code and the model weights are publicly available at https://github.com/NiloofarSafi/TRAC-2. Keywords: Aggression, Misogyny, Abusive Language, Hate-Speech Detection, BERT, NLP, Neural Networks, Social Media},
keywords = {Abusive Language detection},
pubstate = {published},
tppubtype = {inproceedings}
}
In recent times, the focus of the NLP community has increased towards offensive language, aggression, and hate-speech detection.This paper presents our system for TRAC-2 shared task on “Aggression Identification” (sub-task A) and “Misogynistic Aggression Identification” (sub-task B). The data for this shared task is provided in three different languages – English, Hindi, and Bengali. Each data instance is annotated into one of the three aggression classes – Not Aggressive, Covertly Aggressive, Overtly Aggressive, as well as one of the two misogyny classes – Gendered and Non-Gendered. We propose an end-to-end neural model using attention on top of BERT that incorporates a multi-task learning paradigm to address both the sub-tasks simultaneously. Our team, “na14”, scored 0.8579 weighted F1-measure on the English sub-task B and secured 3rd rank out of 15 teams for the task. The code and the model weights are publicly available at https://github.com/NiloofarSafi/TRAC-2. Keywords: Aggression, Misogyny, Abusive Language, Hate-Speech Detection, BERT, NLP, Neural Networks, Social Media
2019
Shafaei, Mahsa; Samghabadi, Niloofar Safi; Kar, Sudipta; Solorio, Thamar
arXiv, (Ed.): 2019, visited: 21.08.2019.
Abstract | Links | BibTeX | Tags: Abusive Language detection, Sentiment analysis, Text Classification
@online{Shafaei2019cb,
title = {Rating for Parents: Predicting Children Suitability Rating for Movies Based on Language of the Movies},
author = {Mahsa Shafaei and Niloofar Safi Samghabadi and Sudipta Kar and Thamar Solorio},
editor = {arXiv},
url = {https://arxiv.org/abs/1908.07819},
year = {2019},
date = {2019-08-21},
urldate = {2019-08-21},
abstract = {The film culture has grown tremendously in recent years. The large number of streaming services put films as one of the most convenient forms of entertainment in today's world. Films can help us learn and inspire societal change. But they can also negatively affect viewers. In this paper, our goal is to predict the suitability of the movie content for children and young adults based on scripts. The criterion that we use to measure suitability is the MPAA rating that is specifically designed for this purpose. We propose an RNN based architecture with attention that jointly models the genre and the emotions in the script to predict the MPAA rating. We achieve 78% weighted F1-score for the classification model that outperforms the traditional machine learning method by 6%.},
keywords = {Abusive Language detection, Sentiment analysis, Text Classification},
pubstate = {published},
tppubtype = {online}
}
The film culture has grown tremendously in recent years. The large number of streaming services put films as one of the most convenient forms of entertainment in today’s world. Films can help us learn and inspire societal change. But they can also negatively affect viewers. In this paper, our goal is to predict the suitability of the movie content for children and young adults based on scripts. The criterion that we use to measure suitability is the MPAA rating that is specifically designed for this purpose. We propose an RNN based architecture with attention that jointly models the genre and the emotions in the script to predict the MPAA rating. We achieve 78% weighted F1-score for the classification model that outperforms the traditional machine learning method by 6%.
2018
Deepthi Mave Niloofar S. Samghabadi, Sudipta Kar
RiTUAL-UH at TRAC 2018 Shared Task: Aggression Identification Inproceedings
In: 2018.
Abstract | Links | BibTeX | Tags: Abusive Language detection, Aggression Identification
@inproceedings{safisamghabadi-EtAl:2018:TRAC1,
title = {RiTUAL-UH at TRAC 2018 Shared Task: Aggression Identification},
author = {Niloofar S. Samghabadi, Deepthi Mave, Sudipta Kar, Thamar Solorio},
url = {http://www.aclweb.org/anthology/W18-4402},
year = {2018},
date = {2018-08-25},
journal = {TRAC1 @ COLING2018},
abstract = {This paper presents our system for “TRAC 2018 Shared Task on Aggression Identification”. Our best systems for the English dataset use a combination of lexical and semantic features. However, for Hindi data using only lexical features gave us the best results. We obtained weighted F1- measures of 0.5921 for the English Facebook task (ranked 12th), 0.5663 for the English Social Media task (ranked 6th), 0.6451 for the Hindi Facebook task (ranked 1st), and 0.4853 for the Hindi Social Media task (ranked 2nd).},
keywords = {Abusive Language detection, Aggression Identification},
pubstate = {published},
tppubtype = {inproceedings}
}
This paper presents our system for “TRAC 2018 Shared Task on Aggression Identification”. Our best systems for the English dataset use a combination of lexical and semantic features. However, for Hindi data using only lexical features gave us the best results. We obtained weighted F1- measures of 0.5921 for the English Facebook task (ranked 12th), 0.5663 for the English Social Media task (ranked 6th), 0.6451 for the Hindi Facebook task (ranked 1st), and 0.4853 for the Hindi Social Media task (ranked 2nd).
2017
Suraj Maharjan Niloofar S. Samghabadi, Alan Sprague
Detecting Nastiness in Social Media Inproceedings
In: ALW1@ACL2017, 2017.
Links | BibTeX | Tags: Abusive Language detection
@inproceedings{safisamghabadi-EtAl:2017:ALW1,
title = {Detecting Nastiness in Social Media},
author = {Niloofar S. Samghabadi, Suraj Maharjan, Alan Sprague, Raquel D. Sprague, Thamar Solorio},
url = {http://aclweb.org/anthology/W17-3010},
year = {2017},
date = {2017-08-04},
booktitle = {ALW1@ACL2017},
keywords = {Abusive Language detection},
pubstate = {published},
tppubtype = {inproceedings}
}
0000
Suraj Maharjan Niloofar S. Samghabadi, Alan Sprague
Detecting Nastiness in Social Media Inproceedings
In: 0000.
BibTeX | Tags: Abusive Language detection, Text Classification
@inproceedings{Safi2017,
title = {Detecting Nastiness in Social Media},
author = {Niloofar S. Samghabadi, Suraj Maharjan, Alan Sprague, Raquel D. Sprague, Thamar Solorio},
keywords = {Abusive Language detection, Text Classification},
pubstate = {published},
tppubtype = {inproceedings}
}
Niloofar Safi Samghabadi Mahsa Shafaei, Sudipta Kar
arXiv, (Ed.): 0000.
Abstract | Links | BibTeX | Tags: Abusive Language detection, Text Classification
@online{Shafaei2019b,
title = {Rating for Parents: Predicting Children Suitability Rating for Movies Based on Language of the Movies},
author = {Mahsa Shafaei, Niloofar Safi Samghabadi, Sudipta Kar, Thamar Solorio},
editor = { arXiv},
url = {https://arxiv.org/abs/1908.07819},
abstract = {The film culture has grown tremendously in recent years. The large number of streaming services put films as one of the most convenient forms of entertainment in today's world. Films can help us learn and inspire societal change. But they can also negatively affect viewers. In this paper, our goal is to predict the suitability of the movie content for children and young adults based on scripts. The criterion that we use to measure suitability is the MPAA rating that is specifically designed for this purpose. We propose an RNN based architecture with attention that jointly models the genre and the emotions in the script to predict the MPAA rating. We achieve 78% weighted F1-score for the classification model that outperforms the traditional machine learning method by 6%.},
keywords = {Abusive Language detection, Text Classification},
pubstate = {published},
tppubtype = {online}
}
The film culture has grown tremendously in recent years. The large number of streaming services put films as one of the most convenient forms of entertainment in today’s world. Films can help us learn and inspire societal change. But they can also negatively affect viewers. In this paper, our goal is to predict the suitability of the movie content for children and young adults based on scripts. The criterion that we use to measure suitability is the MPAA rating that is specifically designed for this purpose. We propose an RNN based architecture with attention that jointly models the genre and the emotions in the script to predict the MPAA rating. We achieve 78% weighted F1-score for the classification model that outperforms the traditional machine learning method by 6%.
Shafaei, Mahsa; Samghabadi, Niloofar Safi; Kar, Sudipta; Solorio, Thamar
arXiv, (Ed.): 0000.
Abstract | Links | BibTeX | Tags: Abusive Language detection, Sentiment analysis, Text Classification
@online{Shafaei2019c,
title = {Rating for Parents: Predicting Children Suitability Rating for Movies Based on Language of the Movies},
author = {Mahsa Shafaei and Niloofar Safi Samghabadi and Sudipta Kar and Thamar Solorio},
editor = {arXiv},
url = {https://arxiv.org/abs/1908.07819},
abstract = {The film culture has grown tremendously in recent years. The large number of streaming services put films as one of the most convenient forms of entertainment in today's world. Films can help us learn and inspire societal change. But they can also negatively affect viewers. In this paper, our goal is to predict the suitability of the movie content for children and young adults based on scripts. The criterion that we use to measure suitability is the MPAA rating that is specifically designed for this purpose. We propose an RNN based architecture with attention that jointly models the genre and the emotions in the script to predict the MPAA rating. We achieve 78% weighted F1-score for the classification model that outperforms the traditional machine learning method by 6%.},
keywords = {Abusive Language detection, Sentiment analysis, Text Classification},
pubstate = {published},
tppubtype = {online}
}
The film culture has grown tremendously in recent years. The large number of streaming services put films as one of the most convenient forms of entertainment in today’s world. Films can help us learn and inspire societal change. But they can also negatively affect viewers. In this paper, our goal is to predict the suitability of the movie content for children and young adults based on scripts. The criterion that we use to measure suitability is the MPAA rating that is specifically designed for this purpose. We propose an RNN based architecture with attention that jointly models the genre and the emotions in the script to predict the MPAA rating. We achieve 78% weighted F1-score for the classification model that outperforms the traditional machine learning method by 6%.