Tootfinder

"How to integrate #Zotero with #Obsidian": https://notelab.hypotheses.org/1266

Linguistically Differentiating Acts and Recalls of Racial Microaggressions on Social Media
Uma Sushmitha Gunturi, Anisha Kumar, Xiaohan Ding, Eugenia H. Rho
https://arxiv.org/abs/2403.16514

Linguistically Differentiating Acts and Recalls of Racial Microaggressions on Social Media
In this work, we examine the linguistic signature of online racial microaggressions (acts) and how it differs from that of personal narratives recalling experiences of such aggressions (recalls) by Black social media users. We manually curate and annotate a corpus of acts and recalls from in-the-wild social media discussions, and verify labels with Black workshop participants. We leverage Natural Language Processing (NLP) and qualitative analysis on this data to classify (RQ1), interpret (RQ2),…

{annotater}: Annotate package load calls, so we can have an idea of the overall purpose of the libraries we’re loading: #rstats

Automatic Annotation of Grammaticality in Child-Caregiver Conversations
Mitja Nikolaus (ILCB, LPL, LIS, TALEP), Abhishek Agrawal (ILCB, LIS, TALEP), Petros Kaklamanis (SED), Alex Warstadt (SED), Abdellah Fourtassi (ILCB, LIS, TALEP)
https://arxiv.org/abs/2403.14208

Automatic Annotation of Grammaticality in Child-Caregiver Conversations
The acquisition of grammar has been a central question to adjudicate between theories of language acquisition. In order to conduct faster, more reproducible, and larger-scale corpus studies on grammaticality in child-caregiver conversations, tools for automatic annotation can offer an effective alternative to tedious manual annotation. We propose a coding scheme for context-dependent grammaticality in child-caregiver conversations and annotate more than 4,000 utterances from a large corpus of t…

IndiTag: An Online Media Bias Analysis and Annotation System Using Fine-Grained Bias Indicators
Luyang Lin, Lingzhi Wang, Jinsong Guo, Jing Li, Kam-Fai Wong
https://arxiv.org/abs/2403.13446

IndiTag: An Online Media Bias Analysis and Annotation System Using Fine-Grained Bias Indicators
In the age of information overload and polarized discourse, understanding media bias has become imperative for informed decision-making and fostering a balanced public discourse. This paper presents IndiTag, an innovative online media bias analysis and annotation system that leverages fine-grained bias indicators to dissect and annotate bias in digital content. IndiTag offers a novel approach by incorporating large language models, bias indicator, vector database to automatically detect and int…

Unveiling A Hidden Risk: Exposing Educational but Malicious Repositories in GitHub
Md Rayhanul MasudUniversity of California, Riverside, Michalis FaloutsosUniversity of California, Riverside
https://arxiv.org/abs/2403.04419

Unveiling A Hidden Risk: Exposing Educational but Malicious Repositories in GitHub
Are malicious repositories hiding under the educational label in GitHub? Recent studies have identified collections of GitHub repositories hosting malware source code with notable collaboration among the developers. Thus, analyzing GitHub repositories deserves inevitable attention due to its open-source nature providing easy access to malicious software code and artifacts. Here we leverage the capabilities of ChatGPT in a qualitative study to annotate an educational GitHub repository based on m…

This https://arxiv.org/abs/2401.00154 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csSI_…

AClassiHonk: A System Framework to Annotate and Classify Vehicular Honk from Road Traffic
Recent studies emphasize that vehicular honking contributes to over 50% of noise pollution in developing urban and suburban areas. Frequent honking negatively impacts health, road safety, and the environment. Recognizing and classifying different vehicle honks could offer valuable insights into environmental noise pollution. Existing research on outdoor sound classification and honk detection lacks the ability to classify honks based on vehicle types, limiting contextual information inference f…

This https://arxiv.org/abs/2401.12729 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCV_…

Enhancing Object Detection Performance for Small Objects through Synthetic Data Generation and Proportional Class-Balancing Technique: A Comparative Study in Industrial Scenarios
Object Detection (OD) has proven to be a significant computer vision method in extracting localized class information and has multiple applications in the industry. Although many of the state-of-the-art (SOTA) OD models perform well on medium and large sized objects, they seem to under perform on small objects. In most of the industrial use cases, it is difficult to collect and annotate data for small objects, as it is time-consuming and prone to human errors. Additionally, those datasets are l…

Leveraging Large Language Models (LLMs) to Support Collaborative Human-AI Online Risk Data Annotation
Jinkyung Park, Pamela Wisniewski, Vivek Singh
https://arxiv.org/abs/2404.07926

Leveraging Large Language Models (LLMs) to Support Collaborative Human-AI Online Risk Data Annotation
In this position paper, we discuss the potential for leveraging LLMs as interactive research tools to facilitate collaboration between human coders and AI to effectively annotate online risk data at scale. Collaborative human-AI labeling is a promising approach to annotating large-scale and complex data for various tasks. Yet, tools and methods to support effective human-AI collaboration for data annotation are under-studied. This gap is pertinent because co-labeling tasks need to support a two…

This https://arxiv.org/abs/2401.16553 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCL_…

SelectLLM: Can LLMs Select Important Instructions to Annotate?
Instruction tuning benefits from large and diverse datasets, however creating such datasets involves a high cost of human labeling. While synthetic datasets generated by large language models (LLMs) have partly solved this issue, they often contain low-quality data. One effective solution is selectively annotating unlabelled instructions, especially given the relative ease of acquiring unlabeled instructions or texts from various sources. However, how to select unlabelled instructions is not we…

A Dataset for Physical and Abstract Plausibility and Sources of Human Disagreement
Annerose Eichel, Sabine Schulte im Walde
https://arxiv.org/abs/2404.04035

A Dataset for Physical and Abstract Plausibility and Sources of Human Disagreement
We present a novel dataset for physical and abstract plausibility of events in English. Based on naturally occurring sentences extracted from Wikipedia, we infiltrate degrees of abstractness, and automatically generate perturbed pseudo-implausible events. We annotate a filtered and balanced subset for plausibility using crowd-sourcing, and perform extensive cleansing to ensure annotation quality. In-depth quantitative analyses indicate that annotators favor plausibility over implausibility and …