TelegramScrap: A comprehensive tool for scraping Telegram data

• URL: http://arxiv.org/abs/2412.16786v1
• Date: Sat, 21 Dec 2024 21:46:56 GMT
• Title: TelegramScrap: A comprehensive tool for scraping Telegram data
• Authors: Ergon Cugler de Moraes Silva,
• Abstract summary: TelegramScrap is a tool for extracting and analyzing data from Telegram channels and groups. This white paper outlines the tool's development, capabilities, and applications in academic and scientific research.
• Score: 0.0
• License:
• Abstract: [WhitePaper] The TelegramScrap tool provides a robust and versatile solution for extracting and analyzing data from Telegram channels and groups, addressing the increasing demand for efficient methods to study digital ecosystems. This white paper outlines the tool's development, capabilities, and applications in academic and scientific research, including studies on disinformation, political communication, and thematic patterns in online communities. Built with flexibility and user accessibility in mind, the tool allows researchers to customize scraping parameters, handle large datasets, and produce structured outputs in formats such as Excel and Parquet. Its modular architecture, real-time progress tracking, and error-handling mechanisms ensure reliability and scalability for diverse research needs. Emphasizing ethical data collection, the tool aligns with Telegram's terms of service and data privacy regulations, encouraging responsible use. Released under an open-source license, TelegramScrap invites the academic community to explore, adapt, and improve the tool while providing appropriate credit. This paper demonstrates the tool's impact through its application in multiple studies, showcasing its potential to advance computational social science and enhance understanding of digital interactions and societal trends [ Code available on GitHub: https://github.com/ergoncugler/web-scraping-telegram ].

Related papers

• pytopicgram: A library for data extraction and topic modeling from Telegram channels [0.0]
  pytopicgram is a Python library that helps researchers collect, organize, and analyze these Telegram messages. pytopicgram allows users to understand how content spreads and how audiences interact on Telegram.
  arXiv  Detail & Related papers  (2025-02-07T12:41:47Z)
• Modeling offensive content detection for TikTok [0.0]
  This research undertakes the collection and analysis of TikTok data containing offensive content. It builds a series of machine learning and deep learning models for offensive content detection.
  arXiv  Detail & Related papers  (2024-08-29T18:47:41Z)
• Leveraging GPT for the Generation of Multi-Platform Social Media Datasets for Research [0.0]
  Social media datasets are essential for research on disinformation, influence operations, social sensing, hate speech detection, cyberbullying, and other significant topics. Access to these datasets is often restricted due to costs and platform regulations. This paper explores the potential of large language models to create lexically and semantically relevant social media datasets across multiple platforms.
  arXiv  Detail & Related papers  (2024-07-11T09:12:39Z)
• Towards Completeness-Oriented Tool Retrieval for Large Language Models [60.733557487886635]
  Real-world systems often incorporate a wide array of tools, making it impractical to input all tools into Large Language Models. Existing tool retrieval methods primarily focus on semantic matching between user queries and tool descriptions. We propose a novel modelagnostic COllaborative Learning-based Tool Retrieval approach, COLT, which captures not only the semantic similarities between user queries and tool descriptions but also takes into account the collaborative information of tools.
  arXiv  Detail & Related papers  (2024-05-25T06:41:23Z)
• Automated Extraction and Maturity Analysis of Open Source Clinical Informatics Repositories from Scientific Literature [0.0]
  This study introduces an automated methodology to bridge the gap by systematically extracting GitHub repository URLs from academic papers indexed in arXiv. Our approach encompasses querying the arXiv API for relevant papers, cleaning extracted GitHub URLs, fetching comprehensive repository information via the GitHub API, and analyzing repository maturity based on defined metrics such as stars, forks, open issues, and contributors.
  arXiv  Detail & Related papers  (2024-03-20T17:06:51Z)
• Privacy-Preserving Graph Machine Learning from Data to Computation: A Survey [67.7834898542701]
  We focus on reviewing privacy-preserving techniques of graph machine learning. We first review methods for generating privacy-preserving graph data. Then we describe methods for transmitting privacy-preserved information.
  arXiv  Detail & Related papers  (2023-07-10T04:30:23Z)
• GAIA Search: Hugging Face and Pyserini Interoperability for NLP Training Data Exploration [97.68234051078997]
  We discuss how Pyserini can be integrated with the Hugging Face ecosystem of open-source AI libraries and artifacts. We include a Jupyter Notebook-based walk through the core interoperability features, available on GitHub. We present GAIA Search - a search engine built following previously laid out principles, giving access to four popular large-scale text collections.
  arXiv  Detail & Related papers  (2023-06-02T12:09:59Z)
• The Semantic Scholar Open Data Platform [79.4493235243312]
  Semantic Scholar (S2) is an open data platform and website aimed at accelerating science by helping scholars discover and understand scientific literature. We combine public and proprietary data sources using state-of-the-art techniques for scholarly PDF content extraction and automatic knowledge graph construction. The graph includes advanced semantic features such as structurally parsed text, natural language summaries, and vector embeddings.
  arXiv  Detail & Related papers  (2023-01-24T17:13:08Z)
• Deep learning for table detection and structure recognition: A survey [49.09628624903334]
  The goal of this survey is to provide a profound comprehension of the major developments in the field of Table Detection. We provide an analysis of both classic and new applications in the field. The datasets and source code of the existing models are organized to provide the reader with a compass on this vast literature.
  arXiv  Detail & Related papers  (2022-11-15T19:42:27Z)
• A Framework for Large Scale Synthetic Graph Dataset Generation [2.248608623448951]
  This work proposes a scalable synthetic graph generation tool to scale the datasets to production-size graphs. The tool learns a series of parametric models from proprietary datasets that can be released to researchers. We demonstrate the generalizability of the framework across a series of datasets.
  arXiv  Detail & Related papers  (2022-10-04T22:41:33Z)
• SOLIS -- The MLOps journey from data acquisition to actionable insights [62.997667081978825]
  In this paper we present a unified deployment pipeline and freedom-to-operate approach that supports all requirements while using basic cross-platform tensor framework and script language engines. This approach however does not supply the needed procedures and pipelines for the actual deployment of machine learning capabilities in real production grade systems.
  arXiv  Detail & Related papers  (2021-12-22T14:45:37Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.