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Lu's Notes
  • Personal paper reading review
  • Data Mining
    • ABF-FNN: A new fuzzy neural network for predicting coal mine gas concentration hazard
    • FNDaaS: Content-agnostic Detection of Fake News sites
    • Robust Fraud Detection via Supervised Contrastive Learning
    • FNDaaS: Content-agnostic Detection of Fake News sites
  • Education
    • Combining Demographic Tabular Data with BERT Outputs for Multilabel Text Classification in Higher Ed
  • Environment
    • Predicting Impression Evaluation of Building Exterior Appearance Using Street Image Big Data and Dee
    • Combat Greenwashing with GoalSpotter: Automatic Sustainability Objective Detection in Heterogeneous
    • A Multi-task Model for Sentiment Aided Stance Detection of Climate Change Tweets
  • LLM
    • Can ChatGPT Reproduce Human-Generated Labels? A Study of Social Computing Tasks
    • What Can Large Language Models Tell Us about Time Series Analysis
  • Library Science
    • Analyzing academic mobility of U.S. professors based on ORCID data and the Carnegie Classificationtl
  • Security
    • How WEIRD is CHI
    • How WEIRD is Usable Privacy and Security Research?
    • Batman Hacked My Password: A Subtitle-Based Analysis of Password Depiction in Movies
    • Darknet
      • “It was honestly just gambling”: Investigating the Experiences of Teenage Cryptocurrency Users on Re
      • Does Online Anonymous Market Vendor Reputation Matter?
    • IllicitPromotion
      • Understanding Cross-Platform Referral Traffic for Illicit Drug Promotion
      • Seeing is Not Always Believing: An Empirical Analysis of Fake Evidence Generators
      • Unveiling the Risks of NFT Promotion Scams
      • Moderating Illicit Online Image Promotion for Unsafe User-Generated Content Games Using Large Vision
      • DarkGram: A Large-Scale Analysis of Cybercriminal Activity Channels on
      • Lifting the Grey Curtain: Analyzing the Ecosystem of Android Scam Apps
  • Social Media
    • Getting Meta A Multimodal Approach for Detecting Unsafe Conversations within Instagram Direct Messag
    • Sliding into My DMs: Detecting Uncomfortable or Unsafe Sexual Risk Experiences within Instagram Dire
    • Cross-Lingual and Cross-Domain Crisis Classification for Low-Resource Scenarios
    • On Xing Tian and the Perseverance of Anti-China Sentiment Online
    • Auditing Algorithmic Explanations of Social Media Feeds: A Case Study of TikTok Video Explanations
    • COVIDSenti: A Large-Scale Benchmark Twitter Data Set for COVID-19 Sentiment Analysis
    • “Go eat a bat, Chang!”: On the Emergence of Sinophobic Behavior on Web Communities in the Face of CO
    • TikTok and the Art of Personalization: Investigating Exploration and Exploitation on Social Media Fe
  • image
    • Title
  • Smart Aquaculture
    • Animal Re-identification
      • An Intelligence Cattle Reidentification System Over Transport by Siamese Neural Networks and YOLO
      • Convolutional Neural Networks for individual identification in the Southern Rock Lobster supply chai
      • Few-Shot Learning for Palmprint Recognition via Meta-Siamese Network
      • Re-identification of fish individuals of undulate skate via deep learning within a few-shot context
    • Computer Vision
      • Adapting Grad-CAM for Embedding Networks
      • CLIP-ReID: Exploiting Vision-Language Model for Image Re-Identificationwithout Concrete Text Labels
      • FastReID: A Pytorch Toolbox for General InstanceRe-identification
      • GW-net: An efficient grad-CAM consistency neural network with weakening of random erasing features f
      • Style Normalization and Restitution for Generalizable Person Re-Identification
    • Hyperspectral Imaging
      • Hyperspectral imaging–based assessment of fresh meat quality: Progress and applications
    • Sensor Data
      • A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anom
      • Big Data Driven Marine Environment Information Forecasting: A Time Series Prediction Network
      • Free nitrous acid prediction in ANAMMOX process using hybrid deep neural network model
      • Predicting ammonia nitrogen in surface water by a new attention-based deep learning hybrid model
      • Prediction of dissolved oxygen in a fishery pond based on gated recurrent unit (GRU)
      • Real-time in situ prediction of ocean currents
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  • Summary
  • Methodology
  1. Environment

Combat Greenwashing with GoalSpotter: Automatic Sustainability Objective Detection in Heterogeneous

PreviousPredicting Impression Evaluation of Building Exterior Appearance Using Street Image Big Data and DeeNextA Multi-task Model for Sentiment Aided Stance Detection of Climate Change Tweets

Last updated 2 months ago

Link: https://dl.acm.org/doi/10.1145/3627673.3680110

Conference: CIKM 2024 Applied Research Papers

Keywords: Greenwashing, Heterogeneous Reports, Text Mining, Transforer

Summary

Build a clustering-based algorithm to extract text elements of heterogeneous sustainability reports (e.g., pdf, html) and group them into informative text blocks. Then build a transforer-based model to detect sustainability objectives in the text blocks.

Methodology

  2. Label

  3. Fine tune Transforer

Discrete textual content processing Solution: clustering

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