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Found 60 papers, 20 papers with code
A Data Bootstrapping Recipe for Low-Resource Multilingual Relation Classification
Data collection is challenging for Indian languages, because they are syntactically and morphologically diverse, as well as different from resource-rich languages like English.
On the rise of fear speech in online social media
Recently, social media platforms are heavily moderated to prevent the spread of online hate speech, which is usually fertile in toxic words and is directed toward an individual or a community.
Diversity matters: Robustness of bias measurements in Wikidata
With the widespread use of knowledge graphs (KG) in various automated AI systems and applications, it is very important to ensure that information retrieval algorithms leveraging them are free from societal biases.
HateProof: Are Hateful Meme Detection Systems really Robust?
Empirically, we find a noticeable performance drop of as high as 10% in the macro-F1 score for certain attacks.
RAFT: Rationale adaptor for few-shot abusive language detection
Abusive language is a concerning problem in online social media.
Hate Speech and Offensive Language Detection in Bengali
To overcome the existing research's limitations, in this study, we develop an annotated dataset of 10K Bengali posts consisting of 5K actual and 5K Romanized Bengali tweets.
Fast Few shot Self-attentive Semi-supervised Political Inclination Prediction
With the rising participation of the common mass in social media, it is increasingly common now for policymakers/journalists to create online polls on social media to understand the political leanings of people in specific locations.
Decoding Demographic un-fairness from Indian Names
Demographic classification is essential in fairness assessment in recommender systems or in measuring unintended bias in online networks and voting systems.
"Dummy Grandpa, do you know anything?": Identifying and Characterizing Ad hominem Fallacy Usage in the Wild
Today, participating in discussions on online forums is extremely commonplace and these discussions have started rendering a strong influence on the overall opinion of online users.
Is this bug severe? A text-cum-graph based model for bug severity prediction
Repositories of large software systems have become commonplace.
Placing (Historical) Facts on a Timeline: A Classification cum Coref Resolution Approach
A timeline provides one of the most effective ways to visualize the important historical facts that occurred over a period of time, presenting the insights that may not be so apparent from reading the equivalent information in textual form.
CounterGeDi: A controllable approach to generate polite, detoxified and emotional counterspeech
We generate counterspeech using three datasets and observe significant improvement across different attribute scores.
HateCheckHIn: Evaluating Hindi Hate Speech Detection Models
To enable more targeted diagnostic insights of such multilingual hate speech models, we introduce a set of functionalities for the purpose of evaluation.
Data Bootstrapping Approaches to Improve Low Resource Abusive Language Detection for Indic Languages
In this paper, to bridge the gap, we demonstrate a large-scale analysis of multilingual abusive speech in Indic languages.
CRUSH: Contextually Regularized and User anchored Self-supervised Hate speech Detection
The last decade has witnessed a surge in the interaction of people through social networking platforms.
FaiRIR: Mitigating Exposure Bias from Related Item Recommendations in Two-Sided Platforms
To this end, our experiments on multiple real-world RIR datasets reveal that the existing RIR algorithms often result in very skewed exposure distribution of items, and the quality of items is not a plausible explanation for such skew in exposure.
hate-alert@DravidianLangTech-ACL2022: Ensembling Multi-Modalities for Tamil TrollMeme Classification
We explore several models to detect Troll memes in Tamil based on the shared task, "Troll Meme Classification in DravidianLangTech2022" at ACL-2022.
Alexa, in you, I trust! Fairness and Interpretability Issues in E-commerce Search through Smart Speakers
While investigating for the fairness of the default action, we observe that over a set of as many as 1000 queries, in nearly 68% cases, there exist one or more products which are more relevant (as per Amazon's own desktop search results) than the product chosen by Alexa.
Two-Face: Adversarial Audit of Commercial Face Recognition Systems
Computer vision applications like automated face detection are used for a variety of purposes ranging from unlocking smart devices to tracking potential persons of interest for surveillance.
Quality change: norm or exception? Measurement, Analysis and Detection of Quality Change in Wikipedia
To the best of our knowledge, this is the first work that rigorously explores English Wikipedia article quality life cycle from the perspective of quality indicators and provides a novel unsupervised page level approach to detect quality switch, which can help in automatic content monitoring in Wikipedia thus contributing significantly to the CSCW community.
A Data Bootstrapping Recipe for Low Resource Multilingual Relation Classification
Relation classification (sometimes called 'extraction') requires trustworthy datasets for fine-tuning large language models, as well as for evaluation.
When expertise gone missing: Uncovering the loss of prolific contributors in Wikipedia
Success of planetary-scale online collaborative platforms such as Wikipedia is hinged on active and continued participation of its voluntary contributors.
You too Brutus! Trapping Hateful Users in Social Media: Challenges, Solutions & Insights
Hate speech is regarded as one of the crucial issues plaguing the online social media.
Debiasing Multilingual Word Embeddings: A Case Study of Three Indian Languages
In this paper, we advance the current state-of-the-art method for debiasing monolingual word embeddings so as to generalize well in a multilingual setting.
Hate-Alert@DravidianLangTech-EACL2021: Ensembling strategies for Transformer-based Offensive language Detection
Social media often acts as breeding grounds for different forms of offensive content.
"Short is the Road that Leads from Fear to Hate": Fear Speech in Indian WhatsApp Groups
We observe that users writing fear speech messages use various events and symbols to create the illusion of fear among the reader about a target community.
When the Umpire is also a Player: Bias in Private Label Product Recommendations on E-commerce Marketplaces
Along a number of our proposed bias measures, we find that the sponsored recommendations are significantly more biased toward Amazon private label products compared to organic recommendations.
Joint Autoregressive and Graph Models for Software and Developer Social Networks
This "social network of code" is rarely studied by social network researchers.
HateXplain: A Benchmark Dataset for Explainable Hate Speech Detection
We also observe that models, which utilize the human rationales for training, perform better in reducing unintended bias towards target communities.
Ranked #3 on
Hate Speech Detection
on HateXplain
Identification, Tracking and Impact: Understanding the trade secret of catchphrases
Understanding the topical evolution in industrial innovation is a challenging problem.
Aspect-based Sentiment Analysis of Scientific Reviews
We also investigate the extent of disagreement between the reviewers and the chair and find that the inter-reviewer disagreement may have a link to the disagreement with the chair.
Gandhipedia: A one-stop AI-enabled portal for browsing Gandhian literature, life-events and his social network
We introduce an AI-enabled portal that presents an excellent visualization of Mahatma Gandhi's life events by constructing temporal and spatial social networks from the Gandhian literature.
Code-switching patterns can be an effective route to improve performance of downstream NLP applications: A case study of humour, sarcasm and hate speech detection
In this paper we demonstrate how code-switching patterns can be utilised to improve various downstream NLP applications.
Deep Learning Models for Multilingual Hate Speech Detection
Hate speech detection is a challenging problem with most of the datasets available in only one language: English.
A deep-learning framework to detect sarcasm targets
In this paper we propose a deep learning framework for sarcasm target detection in predefined sarcastic texts.
HateMonitors: Language Agnostic Abuse Detection in Social Media
In this paper, we present our machine learning model, HateMonitor, developed for Hate Speech and Offensive Content Identification in Indo-European Languages (HASOC), a shared task at FIRE 2019.
Competing Topic Naming Conventions in Quora: Predicting Appropriate Topic Merges and Winning Topics from Millions of Topic Pairs
Our system is also able to predict ~ 25% of the correct case of merges within the first month of the merge and ~ 40% of the cases within a year.
On the Compositionality Prediction of Noun Phrases using Poincar\'e Embeddings
In particular, we use hypernymy information of the multiword and its constituents encoded in the form of the recently introduced Poincar{\'e} embeddings in addition to the distributional information to detect compositionality for noun phrases.
StRE: Self Attentive Edit Quality Prediction in Wikipedia
Wikipedia can easily be justified as a behemoth, considering the sheer volume of content that is added or removed every minute to its several projects.
On the Compositionality Prediction of Noun Phrases using Poincaré Embeddings
In particular, we use hypernymy information of the multiword and its constituents encoded in the form of the recently introduced Poincar\'e embeddings in addition to the distributional information to detect compositionality for noun phrases.
KGPChamps at SemEval-2019 Task 3: A deep learning approach to detect emotions in the dialog utterances.
We report the bidirectional LSTM model, along with the input word embedding as the concatenation of word embedding generated from bidirectional LSTM for word characters and conceptnet embedding, as the best performing model with a highest micro-F1 score of 0. 7261.
DeepTagRec: A Content-cum-User based Tag Recommendation Framework for Stack Overflow
In this paper, we develop a content-cum-user based deep learning framework DeepTagRec to recommend appropriate question tags on Stack Overflow.
A Network-centric Framework for Auditing Recommendation Systems
In this work, we propose a novel network-centric framework which is not only able to quantify various static properties of RSs, but also is able to quantify dynamic properties such as how likely RSs are to lead to polarization or segregation of information among their users.
Hateminers : Detecting Hate speech against Women
With the online proliferation of hate speech, there is an urgent need for systems that can detect such harmful content.
Detecting Reliable Novel Word Senses: A Network-Centric Approach
The outlined method can therefore be used as a new post-hoc step to improve the precision of novel word sense detection in a robust and reliable way where the underlying framework uses a graph structure.
Analyzing the hate and counter speech accounts on Twitter
We also build a supervised model for classifying the hateful and counterspeech accounts on Twitter and obtain an F-score of 0. 77.
Social and Information Networks
Spread of hate speech in online social media
The present online social media platform is afflicted with several issues, with hate speech being on the predominant forefront.
Social and Information Networks
Deep Dive into Anonymity: A Large Scale Analysis of Quora Questions
In particular, we observe that the choice to post the question as anonymous is dependent on the user's perception of anonymity and they often choose to speak about depression, anxiety, social ties and personal issues under the guise of anonymity.
Deep Learning for Social Media Health Text Classification
This paper describes the systems developed for 1st and 2nd tasks of the 3rd Social Media Mining for Health Applications Shared Task at EMNLP 2018.
WikiRef: Wikilinks as a route to recommending appropriate references for scientific Wikipedia pages
In this paper, we propose a novel two step approach -- WikiRef -- that (i) leverages the wikilinks present in a scientific Wikipedia target page and, thereby, (ii) recommends highly relevant references to be included in that target page appropriately and automatically borrowed from the reference section of the wikilinks.
Automated Early Leaderboard Generation From Comparative Tables
We propose several approaches to rank papers from these noisy 'match' outcomes.
Adapting predominant and novel sense discovery algorithms for identifying corpus-specific sense differences
Word senses are not static and may have temporal, spatial or corpus-specific scopes.
AppTechMiner: Mining Applications and Techniques from Scientific Articles
We also categorize individual research articles based on their application areas and the techniques proposed/improved in the article.
All that is English may be Hindi: Enhancing language identification through automatic ranking of the likeliness of word borrowing in social media
Based on this likeliness estimate we asked annotators to re-annotate the language tags of foreign words in predominantly native contexts.
All that is English may be Hindi: Enhancing language identification through automatic ranking of likeliness of word borrowing in social media
Based on this likeliness estimate we asked annotators to re-annotate the language tags of foreign words in predominantly native contexts.
Is this word borrowed? An automatic approach to quantify the likeliness of borrowing in social media
We first propose context based clustering method to sample a set of candidate words from the social media data. Next, we propose three novel and similar metrics based on the usage of these words by the users in different tweets; these metrics were used to score and rank the candidate words indicating their borrowed likeliness.
Language Use Matters: Analysis of the Linguistic Structure of Question Texts Can Characterize Answerability in Quora
Notably, features representing the language use patterns of the users are most discriminative and alone account for an accuracy of 74. 18%.
Which techniques does your application use?: An information extraction framework for scientific articles
Every field of research consists of multiple application areas with various techniques routinely used to solve problems in these wide range of application areas.
WASSUP? LOL : Characterizing Out-of-Vocabulary Words in Twitter
Language in social media is mostly driven by new words and spellings that are constantly entering the lexicon thereby polluting it and resulting in high deviation from the formal written version.
That's sick dude!: Automatic identification of word sense change across different timescales
In this paper, we propose an unsupervised method to identify noun sense changes based on rigorous analysis of time-varying text data available in the form of millions of digitized books.
