Pushpak Bhattacharyya
Biographic Data
| ID | 6536742 |
|---|---|
| NAME | Pushpak Bhattacharyya |
| GIVEN NAMES | Pushpak |
| FAMILY NAME | Bhattacharyya |
| SIGNATURE | BHATTACHARYYA P |
| AFFILIATIONS | Indian Institute of Technology Bombay |
| ORCID | 0000-0001-5319-5508 |
| VERIFIED | Yes |
| TOTAL WORKS | 16 |
| TOTAL CITATIONS | 1 |
| AUTHOR COUNT | 16 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2020 |
| LATEST PUBLICATION YEAR | 2024 |
| H-INDEX | 1 |
MTBullyGNN: A Graph Neural Network-Based Multitask Framework for Cyberbullying Detection
Cyberbullying is a malady of social media, and its automatic detection is critically important considering its virulence, velocity of spreading, and the scale of the havoc it can wreak. However, the problem is challenging due to its disguised behavior, noise in the content, and, in recent times, introduction of code-mixing. In this work, we propose MTBullyGNN a novel graph neural network (GNN)-based multitask (MT) framework that solves sentiment-…
Being Polite: Modeling Politeness Variation in a Personalized Dialog Agent
Politeness enhances interactions by improving relations between the participants. If there is a display of rudeness, even the finest conversation can fall through. In addition, if lathered with kindness, even the most angst-prone circumstance can be expressed with far less suffering. Previously, researchers have focused upon including politeness in conversations. But the existing research does not focus on variations in politeness according to th…
Mental Health Disorder Identification From Motivational Conversations
Mental health disorder continues to be a grievous concern plaguing humans worldwide. The scarcity of mental health professionals (MHPs) has driven novel efforts lately to combat mental illness by developing automated systems capable of assisting MHPs. However, lack of high-quality conversational data due to privacy concerns remains a bottleneck toward its study and automation. Also, distinguishing and identifying various mental disorders is a cha…
Emoji, Sentiment and Emotion Aided Cyberbullying Detection in Hinglish
The advent of the Internet is a boon to society. However, many of its banes cannot be undermined, cyberbullying being one of them. The emotional state and sentiment of a person have a significant influence on the intended content. The current work is the first attempt in investigating the role of sentiment and emotion information for identifying cyberbullying in the Indian scenario. From Twitter, a benchmark Hind–English code-mixed corpus called …
Sehc: A Benchmark Setup to Identify Online Hate Speech in English
Thanks to the digital age, online speech and information may now be disseminated anonymously without regard for repercussions. Regulators face a unique problem with social media platforms because of the speed and volume of material and the lack of editorial supervision. The existing datasets on hate speech or offensive language identification lack diversity in the dataset’s content. In this article, we create a multi-domain hate speech corpus (MH…
Investigations in Emotion Aware Multimodal Gender Prediction Systems From Social Media Data
Gender plays a crucial role in improving the performance quality of personalized systems. Privacy and anonymity allow users to hide their details. Based on the intuition that post contents of male and female users differ, we can predict the gender of the social media account holder via their corresponding posts. These posts can be multimodal (text + image) in nature. We investigate various emotion-assisted multimodal gender prediction models in t…
An Attention-Based Multimodal Siamese Architecture for Tweet-User Verification
With the advent of internet technologies, it has created different ways of writing anonymously, which has lead to criminal and malicious activities over social media platforms. Thus, the automatic authentication checking of the available contents is the need of the hour. Social media sites, such as Facebook, Twitter, and so on, are used heavily by the users for sharing of information about their day-to-day activities. The identity of the suspect …
Unity in Diversity: Multilabel Emoji Identification in Tweets
Emojis or emoticons are not just a modern trend but have become an essential part of our day-to-day interactions. Predicting a suitable emoji for a given tweet is a challenging task because a wrong emoji prediction for a tweet can change the meaning of the message or can amplify the emotion of the message. This task is particularly challenging since it requires selecting an appropriate emoji from a huge list of prospective emojis that may or may …
On Multimodal Microblog Summarization
Microblog summarization systems are gaining importance during natural disasters. A lot of tweets are posted along with multimedia content during the occurrence of any natural disaster event. Extracting relevant information/summary from these tweets is important for the smooth functioning of the rescue operation. Moreover, because of the limited size of the tweets, in many cases, tweets are associated with images. The current work is the first of …
A Multitask Multimodal Ensemble Model for Sentiment- and Emotion-Aided Tweet Act Classification
Speech act classification determining the communicative intent of an utterance has been studied widely over the years as an independent task. This holds true for discussion in any for a, including social media platforms such as Twitter. However, the tweeter’s emotional state has a huge impact on its pragmatic content because communication is fundamentally characterized and mediated by direct emotions. Sentiment as a human behavior often has a str…
What Does Your Bio Say? Inferring Twitter Users’ Depression Status From Multimodal Profile Information Using Deep Learning
People suffering from stress and various mental health problems find it easier to express and share their feelings on online platforms, such as Twitter. However, the imposed character limit (280 characters) by Twitter and infrequent online activities of a section of users poses a serious setback in using computational methods for mental health analysis or emotion research. Twitter provides rich metadata information about its users (such as user’s…
Authorship Attribution of Microtext Using Capsule Networks
Authorship attribution (AA) is an important task, as it identifies the author of a written text from a set of suspect authors. Different methodologies of anonymous writing have been discovered with the rising usage of social media. This anonymous writing leads to an increase in malicious and suspicious activities, and anonymity makes it difficult to find the suspect. AA helps to find the writer of a suspect text from a set of suspects. Different …
A Multimodal Author Profiling System for Tweets
The rising usage of social media has motivated to invent different methodologies of anonymous writing, which leads to an increase in malicious and suspicious activities. This anonymity has created difficulty in finding the suspect. Author profiling deals with the characterization of an author through some key attributes such as gender, age, language, dialect region variety, personality, and so on. Identifying the gender of the author of a suspect…
Novelty Detection: A Perspective from Natural Language Processing
The quest for new information is an inborn human trait and has always been quintessential for human survival and progress. Novelty drives curiosity, which in turn drives innovation. In Natural Language Processing (NLP), Novelty Detection refers to finding text that has some new information to offer with respect to whatever is earlier seen or known. With the exponential growth of information all across the Web, there is an accompanying menace of r…
Bert-Caps: A Transformer-Based Capsule Network for Tweet Act Classification
Identification of speech acts provides essential cues in understanding the pragmatics of a user utterance. It typically helps in comprehending the communicative intention of a speaker. This holds true for conversations or discussions on any fora, including social media platforms, such as Twitter. This article presents a novel tweet act classifier (speech act for Twitter) for assessing the content and intent of tweets, thereby exploring the valuab…
Indowordnet’s help in Indian language machine translation
Novelty Detection: A Perspective from Natural Language Processing
The quest for new information is an inborn human trait and has always been quintessential for human survival and progress. Novelty drives curiosity, which in turn drives innovation. In Natural Language Processing (NLP), Novelty Detection refers to finding text that has some new information to offer with respect to whatever is earlier seen or known. With the exponential growth of information all across the Web, there is an accompanying menace of r…
Bert-Caps: A Transformer-Based Capsule Network for Tweet Act Classification
Identification of speech acts provides essential cues in understanding the pragmatics of a user utterance. It typically helps in comprehending the communicative intention of a speaker. This holds true for conversations or discussions on any fora, including social media platforms, such as Twitter. This article presents a novel tweet act classifier (speech act for Twitter) for assessing the content and intent of tweets, thereby exploring the valuab…
Indowordnet’s help in Indian language machine translation
A Multimodal Author Profiling System for Tweets
The rising usage of social media has motivated to invent different methodologies of anonymous writing, which leads to an increase in malicious and suspicious activities. This anonymity has created difficulty in finding the suspect. Author profiling deals with the characterization of an author through some key attributes such as gender, age, language, dialect region variety, personality, and so on. Identifying the gender of the author of a suspect…
Novelty Detection: A Perspective from Natural Language Processing
The quest for new information is an inborn human trait and has always been quintessential for human survival and progress. Novelty drives curiosity, which in turn drives innovation. In Natural Language Processing (NLP), Novelty Detection refers to finding text that has some new information to offer with respect to whatever is earlier seen or known. With the exponential growth of information all across the Web, there is an accompanying menace of r…
On Multimodal Microblog Summarization
Microblog summarization systems are gaining importance during natural disasters. A lot of tweets are posted along with multimedia content during the occurrence of any natural disaster event. Extracting relevant information/summary from these tweets is important for the smooth functioning of the rescue operation. Moreover, because of the limited size of the tweets, in many cases, tweets are associated with images. The current work is the first of …
A Multitask Multimodal Ensemble Model for Sentiment- and Emotion-Aided Tweet Act Classification
Speech act classification determining the communicative intent of an utterance has been studied widely over the years as an independent task. This holds true for discussion in any for a, including social media platforms such as Twitter. However, the tweeter’s emotional state has a huge impact on its pragmatic content because communication is fundamentally characterized and mediated by direct emotions. Sentiment as a human behavior often has a str…
What Does Your Bio Say? Inferring Twitter Users’ Depression Status From Multimodal Profile Information Using Deep Learning
People suffering from stress and various mental health problems find it easier to express and share their feelings on online platforms, such as Twitter. However, the imposed character limit (280 characters) by Twitter and infrequent online activities of a section of users poses a serious setback in using computational methods for mental health analysis or emotion research. Twitter provides rich metadata information about its users (such as user’s…
Authorship Attribution of Microtext Using Capsule Networks
Authorship attribution (AA) is an important task, as it identifies the author of a written text from a set of suspect authors. Different methodologies of anonymous writing have been discovered with the rising usage of social media. This anonymous writing leads to an increase in malicious and suspicious activities, and anonymity makes it difficult to find the suspect. AA helps to find the writer of a suspect text from a set of suspects. Different …
Being Polite: Modeling Politeness Variation in a Personalized Dialog Agent
Politeness enhances interactions by improving relations between the participants. If there is a display of rudeness, even the finest conversation can fall through. In addition, if lathered with kindness, even the most angst-prone circumstance can be expressed with far less suffering. Previously, researchers have focused upon including politeness in conversations. But the existing research does not focus on variations in politeness according to th…
Mental Health Disorder Identification From Motivational Conversations
Mental health disorder continues to be a grievous concern plaguing humans worldwide. The scarcity of mental health professionals (MHPs) has driven novel efforts lately to combat mental illness by developing automated systems capable of assisting MHPs. However, lack of high-quality conversational data due to privacy concerns remains a bottleneck toward its study and automation. Also, distinguishing and identifying various mental disorders is a cha…
Emoji, Sentiment and Emotion Aided Cyberbullying Detection in Hinglish
The advent of the Internet is a boon to society. However, many of its banes cannot be undermined, cyberbullying being one of them. The emotional state and sentiment of a person have a significant influence on the intended content. The current work is the first attempt in investigating the role of sentiment and emotion information for identifying cyberbullying in the Indian scenario. From Twitter, a benchmark Hind–English code-mixed corpus called …
Sehc: A Benchmark Setup to Identify Online Hate Speech in English
Thanks to the digital age, online speech and information may now be disseminated anonymously without regard for repercussions. Regulators face a unique problem with social media platforms because of the speed and volume of material and the lack of editorial supervision. The existing datasets on hate speech or offensive language identification lack diversity in the dataset’s content. In this article, we create a multi-domain hate speech corpus (MH…
Investigations in Emotion Aware Multimodal Gender Prediction Systems From Social Media Data
Gender plays a crucial role in improving the performance quality of personalized systems. Privacy and anonymity allow users to hide their details. Based on the intuition that post contents of male and female users differ, we can predict the gender of the social media account holder via their corresponding posts. These posts can be multimodal (text + image) in nature. We investigate various emotion-assisted multimodal gender prediction models in t…
An Attention-Based Multimodal Siamese Architecture for Tweet-User Verification
With the advent of internet technologies, it has created different ways of writing anonymously, which has lead to criminal and malicious activities over social media platforms. Thus, the automatic authentication checking of the available contents is the need of the hour. Social media sites, such as Facebook, Twitter, and so on, are used heavily by the users for sharing of information about their day-to-day activities. The identity of the suspect …
Unity in Diversity: Multilabel Emoji Identification in Tweets
Emojis or emoticons are not just a modern trend but have become an essential part of our day-to-day interactions. Predicting a suitable emoji for a given tweet is a challenging task because a wrong emoji prediction for a tweet can change the meaning of the message or can amplify the emotion of the message. This task is particularly challenging since it requires selecting an appropriate emoji from a huge list of prospective emojis that may or may …
MTBullyGNN: A Graph Neural Network-Based Multitask Framework for Cyberbullying Detection
Cyberbullying is a malady of social media, and its automatic detection is critically important considering its virulence, velocity of spreading, and the scale of the havoc it can wreak. However, the problem is challenging due to its disguised behavior, noise in the content, and, in recent times, introduction of code-mixing. In this work, we propose MTBullyGNN a novel graph neural network (GNN)-based multitask (MT) framework that solves sentiment-…
Artificial Intelligence (16 works) · Computer Science (16 works) · Natural language processing (13 works) · World Wide Web (11 works) · Machine learning (10 works) · Social media (10 works) · Topic Modeling (9 works) · Psychology (8 works) · Hate Speech and Cyberbullying Detection (6 works) · Information retrieval (5 works)