Research
On-device research index

arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

169,051 papers · 148 categories

Trend · papers per month

count% of corpus
0111 · Jan 201919922001200920172026
4 results for “sexism”
How is Your Mood When Writing Sexist tweets? Detecting the Emotion Type and Intensity of Emotion Using Natural Language Processing Techniquescs.SI

Study categorizes and analyzes emotions in sexist tweets.

problem Lack of defined categories for sexism in NLP.
method Used a new dataset from SemEval-2018 to classify and analyze emotions in sexist tweets.
result Demonstrated the mental state and affectual state of users who tweet in different categories of sexism.
sexismemotionsnatural language processingsentiment analysistwitter
2019-01-28abs ↗pdf ↗
Attention-based method for categorizing different types of online harassment languagecs.CL

Study uses attention-based method to detect different types of online harassment.

problem Detecting different types of online harassment in social media content.
method Multi-attention based approach using Recurrent Neural Networks to address imbalanced data.
result Demonstrates effectiveness of attention-based mechanism for detecting various types of online harassment.
Online harassmentSocial mediaRecurrent Neural NetworksMulti-attentionImbalanced data
2019-09-28abs ↗pdf ↗
"Centralized or Decentralized?": Concerns and Value Judgments of Stakeholders in the Non-Fungible Tokens (NFTs) Marketcs.CY

NFTs raise concerns like scams, racism, and sexism; centralization vs decentralization debate.

problem Concerns and value judgments of stakeholders in NFT market.
method Mixed quantitative and qualitative methods: social media analysis and interviews.
result Identified financial scams, counterfeit NFTs, hacking, and unethical NFTs as major issues.
NFTsdecentralizationstakeholdersscamscounterfeit
2023-11-18abs ↗pdf ↗
AI and ethics in insurance: a new solution to mitigate proxy discrimination in risk modelingstat.ML

New method reduces indirect discrimination in insurance risk models.

problem Indirect discrimination in insurance risk models using machine learning.
method Mathematical concepts of linear algebra to reduce indirect discrimination.
result Demonstrated promising performance in a concrete case of risk selection in life insurance.
insurancemachine learningdiscriminationrisk modelinglinear algebra
2023-07-25abs ↗pdf ↗