arXiv subjects

Shomir Wilson

Publications and source records attributed to Shomir Wilson.

24 records · Page 2Linked to original sources

arXiv2023

Nationality Bias in Text Generation

Little attention is placed on analyzing nationality bias in language models, especially when nationality is highly used as a factor in increasing the performance of social NLP models. This paper examines how a text generation model, GPT-2, accentuates pre-existing societal biases about country-based demonyms. We generate stories using GPT-2 for various nationalities and use sensitivity analysis to explore how the number of internet users and the country's economic status impacts the sentiment of the stories. To reduce the propagation of biases through large language models (LLM), we explore the debiasing method of adversarial triggering. Our results show that GPT-2 demonstrates significant bias against countries with lower internet users, and adversarial triggering effectively reduces the same.

cs.CL↗

arXiv2022

Creation and Analysis of an International Corpus of Privacy Laws

The landscape of privacy laws and regulations around the world is complex and ever-changing. National and super-national laws, agreements, decrees, and other government-issued rules form a patchwork that companies must follow to operate internationally. To examine the status and evolution of this patchwork, we introduce the Government Privacy Instructions Corpus, or GPI Corpus, of 1,043 privacy laws, regulations, and guidelines, covering 182 jurisdictions. This corpus enables a large-scale quantitative and qualitative examination of legal foci on privacy. We examine the temporal distribution of when GPIs were created and illustrate the dramatic increase in privacy legislation over the past 50 years, although a finer-grained examination reveals that the rate of increase varies depending on the personal data types that GPIs address. Our exploration also demonstrates that most privacy laws respectively address relatively few personal data types, showing that comprehensive privacy legislation remains rare. Additionally, topic modeling results show the prevalence of common themes in GPIs, such as finance, healthcare, and telecommunications. Finally, we release the corpus to the research community to promote further study.

cs.CL↗

arXiv2022

Automated Detection of Doxing on Twitter

Doxing refers to the practice of disclosing sensitive personal information about a person without their consent. This form of cyberbullying is an unpleasant and sometimes dangerous phenomenon for online social networks. Although prior work exists on automated identification of other types of cyberbullying, a need exists for methods capable of detecting doxing on Twitter specifically. We propose and evaluate a set of approaches for automatically detecting second- and third-party disclosures on Twitter of sensitive private information, a subset of which constitutes doxing. We summarize our findings of common intentions behind doxing episodes and compare nine different approaches for automated detection based on string-matching and one-hot encoded heuristics, as well as word and contextualized string embedding representations of tweets. We identify an approach providing 96.86% accuracy and 97.37% recall using contextualized string embeddings and conclude by discussing the practicality of our proposed methods.

cs.SI↗

arXiv2021

Identification of Bias Against People with Disabilities in Sentiment Analysis and Toxicity Detection Models

Sociodemographic biases are a common problem for natural language processing, affecting the fairness and integrity of its applications. Within sentiment analysis, these biases may undermine sentiment predictions for texts that mention personal attributes that unbiased human readers would consider neutral. Such discrimination can have great consequences in the applications of sentiment analysis both in the public and private sectors. For example, incorrect inferences in applications like online abuse and opinion analysis in social media platforms can lead to unwanted ramifications, such as wrongful censoring, towards certain populations. In this paper, we address the discrimination against people with disabilities, PWD, done by sentiment analysis and toxicity classification models. We provide an examination of sentiment and toxicity analysis models to understand in detail how they discriminate PWD. We present the Bias Identification Test in Sentiments (BITS), a corpus of 1,126 sentences designed to probe sentiment analysis models for biases in disability. We use this corpus to demonstrate statistically significant biases in four widely used sentiment analysis tools (TextBlob, VADER, Google Cloud Natural Language API and DistilBERT) and two toxicity analysis models trained to predict toxic comments on Jigsaw challenges (Toxic comment classification and Unintended Bias in Toxic comments). The results show that all exhibit strong negative biases on sentences that mention disability. We publicly release BITS Corpus for others to identify potential biases against disability in any sentiment analysis tools and also to update the corpus to be used as a test for other sociodemographic variables as well.

cs.CL↗

arXiv2021

A `Sourceful' Twist: Emoji Prediction Based on Sentiment, Hashtags and Application Source

We widely use emojis in social networking to heighten, mitigate or negate the sentiment of the text. Emoji suggestions already exist in many cross-platform applications but an emoji is predicted solely based a few prominent words instead of understanding the subject and substance of the text. Through this paper, we showcase the importance of using Twitter features to help the model understand the sentiment involved and hence to predict the most suitable emoji for the text. Hashtags and Application Sources like Android, etc. are two features which we found to be important yet underused in emoji prediction and Twitter sentiment analysis on the whole. To approach this shortcoming and to further understand emoji behavioral patterns, we propose a more balanced dataset by crawling additional Twitter data, including timestamp, hashtags, and application source acting as additional attributes to the tweet. Our data analysis and neural network model performance evaluations depict that using hashtags and application sources as features allows to encode different information and is effective in emoji prediction.

cs.CL↗

arXiv2019

Question Answering for Privacy Policies: Combining Computational and Legal Perspectives

Privacy policies are long and complex documents that are difficult for users to read and understand, and yet, they have legal effects on how user data is collected, managed and used. Ideally, we would like to empower users to inform themselves about issues that matter to them, and enable them to selectively explore those issues. We present PrivacyQA, a corpus consisting of 1750 questions about the privacy policies of mobile applications, and over 3500 expert annotations of relevant answers. We observe that a strong neural baseline underperforms human performance by almost 0.3 F1 on PrivacyQA, suggesting considerable room for improvement for future systems. Further, we use this dataset to shed light on challenges to question answerability, with domain-general implications for any question answering system. The PrivacyQA corpus offers a challenging corpus for question answering, with genuine real-world utility.

cs.CL↗