Papers Author Profiling

Author-Specific Linguistic Patterns Unveiled: A Deep Learning Study on Word Class Distributions

Deep learning methods have been increasingly applied to computational linguistics to uncover patterns in text data. This study investigates author-specific word class distributions using part-of-speech (POS) tagging and …

Recipient Profiling: Predicting Characteristics from Messages

It has been shown in the field of Author Profiling that texts may inadvertently reveal sensitive information about their authors, such as gender or age. This raises important privacy concerns that have been extensively a…

BN-AuthProf: Benchmarking Machine Learning for Bangla Author Profiling on Social Media Texts

Author profiling, the analysis of texts to uncover attributes such as gender and age of the author, has become essential with the widespread use of social media platforms. This paper focuses on author profiling in the Ba…

Prompt-based Personality Profiling: Reinforcement Learning for Relevance Filtering

Author profiling is the task of inferring characteristics about individuals by analyzing content they share. Supervised machine learning still dominates automatic systems that perform this task, despite the popularity of…

A Synthetic Dataset for Personal Attribute Inference

Recently, powerful Large Language Models (LLMs) have become easily accessible to hundreds of millions of users world-wide. However, their strong capabilities and vast world knowledge do not come without associated privac…

User-Centered Security in Natural Language Processing

This dissertation proposes a framework of user-centered security in Natural Language Processing (NLP), and demonstrates how it can improve the accessibility of related research. Accordingly, it focuses on two security do…

GetSmartMSEC at SemEval-2022 Task 6: Sarcasm Detection using Contextual Word Embedding with Gaussian model for Irony Type Identification

Sarcasm refers to the use of words that have different literal and intended meanings. It represents the usage of words that are opposite of what is literally said, especially in order to insult, mock, criticise or irrita…

CS-UM6P at SemEval-2022 Task 6: Transformer-based Models for Intended Sarcasm Detection in English and Arabic

Sarcasm is a form of figurative language where the intended meaning of a sentence differs from its literal meaning. This poses a serious challenge to several Natural Language Processing (NLP) applications such as Sentime…

UMUTextStats: A linguistic feature extraction tool for Spanish

Feature Engineering consists in the application of domain knowledge to select and transform relevant features to build efficient machine learning models. In the Natural Language Processing field, the state of the art con…

Zero and Few-shot Learning for Author Profiling

Author profiling classifies author characteristics by analyzing how language is shared among people. In this work, we study that task from a low-resource viewpoint: using little or no training data. We explore different …

Items from Psychometric Tests as Training Data for Personality Profiling Models of Twitter Users

Machine-learned models for author profiling in social media often rely on data acquired via self-reporting-based psychometric tests (questionnaires) filled out by social media users. This is an expensive but accurate dat…

How Different Text-preprocessing Techniques Using The BERT Model Affect The Gender Profiling of Authors

Forensic author profiling plays an important role in indicating possible profiles for suspects. Among the many automated solutions recently proposed for author profiling, transfer learning outperforms many other state-of…

Unified and Multilingual Author Profiling for Detecting Haters

This paper presents a unified user profiling framework to identify hate speech spreaders by processing their tweets regardless of the language. The framework encodes the tweets with sentence transformers and applies an a…

Preventing Author Profiling through Zero-Shot Multilingual Back-Translation

Documents as short as a single sentence may inadvertently reveal sensitive information about their authors, including e.g. their gender or ethnicity. Style transfer is an effective way of transforming texts in order to r…

UL2C: Mapping User Locations to Countries on Arabic Twitter

Mapping user locations to countries can be useful for many applications such as dialect identification, author profiling, recommendation system, etc. Twitter allows users to declare their locations as free text, and thes…

Adversarial Stylometry in the Wild: Transferable Lexical Substitution Attacks on Author Profiling

Written language contains stylistic cues that can be exploited to automatically infer a variety of potentially sensitive author information. Adversarial stylometry intends to attack such models by rewriting an author's t…

SpanEmo: Casting Multi-label Emotion Classification as Span-prediction

Emotion recognition (ER) is an important task in Natural Language Processing (NLP), due to its high impact in real-world applications from health and well-being to author profiling, consumer analysis and security. Curren…

Too good to be true? Predicting author profiles from abusive language

The problem of online threats and abuse could potentially be mitigated with a computational approach, where sources of abuse are better understood or identified through author profiling. However, abusive language constit…

Constructing a Bilingual Corpus of Parallel Tweets

In a bid to reach a larger and more diverse audience, Twitter users often post parallel tweets{---}tweets that contain the same content but are written in different languages. Parallel tweets can be an important resource…

Proceedings of the Workshop on Resources and Techniques for User and Author Profiling in Abusive Language