{"id":"f6d3bc7e-409b-453f-ba39-cbdf2b22fab2","arxiv_id":"2412.16786","paper_version":1,"verdict":"UNVERDICTED","confidence":"MODERATE","novelty_score":3.0,"correctness_risk":"medium","formal_verification":"none","parameter_count":0,"one_line_summary":"A white paper describes a Telegram scraping tool built on Telethon, listing its features and the author's prior studies that used it.","lead":"This white paper presents TelegramScrap, a Python tool for scraping messages and metadata from Telegram channels and groups using the Telethon library. It targets researchers in computational social science, but provides no benchmarks or independent evaluation of the tool.","discovery_kind":"extension","skeptic_critique":{"model":"deepseek-v4-flash","headline":"The code in §3 never calls `client.start()` or `sign_in()`, so a fresh Colab session cannot authenticate; the printed tool fails at the first `iter_messages` call.","rationale":"The reader correctly identified that the printed code's runnability is load-bearing. My static analysis of Table 03 sharpens this: the core `async with TelegramClient(...)` block never invokes `client.start()` or `sign_in()`, so in a fresh Colab runtime the client is connected but unauthenticated. The first `iter_messages` call raises an authorization error, so the tool cannot scrape. This is not a stylistic concern or a disagreement with community norms; it is an internal inconsistency in the code listing itself. I therefore recommend rejecting the paper in its current form (or, at minimum, requiring a version that calls `start()` and demonstrates a successful end-to-end scrape). Because the reader's verdict was already UNVERDICTED, one could argue for no change; however, the presence of a concrete fatal defect moves the assessment from 'not verified' to 'verified to be broken as printed'.","tokens_in":23224,"tokens_out":10058,"duration_ms":79664,"concrete_test":"Run the three cells exactly as printed in a fresh Google Colab notebook with a real Telegram account and a single public channel; if the run fails with an authorization error before any message is printed, the concern lands. If, instead, the script prompts for a code, authenticates, and scrapes messages, the concern is refuted. Alternatively, a static check is sufficient: search the third cell in the PDF for `client.start(` or `client.sign_in(`; the absence of both confirms the defect without running code.","verdict_should_be":"REJECT","load_bearing_attack":"Table 03's core block opens `async with TelegramClient(username, api_id, api_hash) as client:` and immediately calls `client.iter_messages(...)`. Telethon's async context manager only connects; it does not start or authenticate the session. In a fresh Google Colab runtime there is no existing `.session` file, and the setup instructions in §3.I do not create one. Consequently the first API call raises `UnauthorizedError` (or `AuthKeyUnregisteredError`) and no messages are scraped. The paper's statement in §2 that 'Telegram may request a verification code during the process' has no corresponding code path: there is no `client.start(phone=...)`, no `code_callback`, and no `sign_in` anywhere in the listing. This is not a style issue; it is a load-bearing failure of the central claim that the tool 'provides a robust and versatile solution...'. If a prior authorized session file existed, the code could run, but the paper's own 'Set up your credentials once' step does not generate one. Thus the paper's strongest claim is unsupported by the code as printed.","agreement_with_reader":"agree"},"referee_report":{"model":"deepseek-v4-flash","summary":"The paper describes TelegramScrap, an open-source tool for scraping messages, metadata, and comments from Telegram channels and groups using the Telethon library in Google Colab. It provides three code cells for credential setup, parameter configuration, and scraping, and claims that the tool is robust, versatile, and scalable. The paper also lists numerous prior studies and institutional investigations that purportedly used the tool, and concludes with an invitation for adoption in social-science research.","tokens_in":23448,"tokens_out":8503,"duration_ms":66381,"significance":"TelegramScrap addresses a real need in computational social science: a configurable, open-source scraper for Telegram data. The paper's clear step-by-step presentation and the availability of a GitHub repository are strengths, as are the explicit cautions about Colab runtime limits and Telegram API softbans. However, the paper provides no tests, benchmarks, or worked examples, and the code listing as printed contains at least two runtime errors that would prevent it from scraping anything in a fresh Colab session. These issues directly undermine the central claim of robustness and reliability, so the contribution is not yet demonstrated.","major_comments":[{"comment":"The scraping cell opens `async with TelegramClient(username, api_id, api_hash) as client:` and immediately calls `client.iter_messages(channel, ...)`. In Telethon, the async context manager only connects; it does not authenticate the session. There is no `client.start()`, `sign_in()`, or `code_callback` anywhere in the listing, and the 'Set up your credentials once' step in §3.I does not create a `.session` file. Consequently, in a fresh Colab runtime the first API call will raise `UnauthorizedError` (or `AuthKeyUnregisteredError`), and no messages will be scraped. The claim in §2.III that 'Telegram may request a verification code during the process' has no corresponding code path.","section":"§3 Table 03 and §2.III"},{"comment":"The first cell imports `from telethon.sync import TelegramClient`, but the third cell uses the client as an asynchronous context manager with `async with` and `async for`. The `telethon.sync` module is intended for synchronous usage; its `iter_messages` returns a synchronous generator, so the `async for` loop in Table 03 will fail at runtime because a synchronous generator is not an async iterator. The paper's note in §2 that Google Colab can 'run async without needing to define them within an async def' does not resolve this inconsistency, because the import itself selects the synchronous API.","section":"§3 Table 01 vs Table 03"},{"comment":"The progress formula `current_progress = t_index/(t_index+message_id) if (t_index+message_id) <= max_t_index else t_index/max_t_index` does not measure progress. `message_id` is a Telegram message identifier, which can be arbitrarily large, so the ratio is not bounded by 1 and is unrelated to the number of messages processed. The else-branch uses `t_index/max_t_index`, but the first branch makes the displayed percentage, estimated total time, and remaining time meaningless. This undermines the advertised 'real-time progress tracking' feature.","section":"§3 Table 03, `print_progress`"},{"comment":"The central claims that the tool is 'robust,' 'versatile,' and 'reliable' are not substantiated by any test, benchmark, or reproducible example. The paper lists applications in prior studies and institutional settings, but it does not report a single run of the code, nor does it specify Python versions, Telethon versions, required packages beyond Telethon, or a sample dataset. The omission is load-bearing because a tool paper's central claim is that the code works as described; without a minimal verification, the claims in the abstract cannot be evaluated.","section":"Abstract, §4"}],"minor_comments":[{"comment":"The paper refers to Figure 1, Figure 2, and Figure 3 in §2, but the full text does not display these figures; only captions are present.","section":"§2"},{"comment":"The title contains a typo: 'Acomprehensive' should be 'A comprehensive'.","section":"Title"},{"comment":"The paper is bilingual (English and Portuguese) with duplicated content; this makes the manuscript longer than necessary and may reduce readability for reviewers.","section":"Whole paper"},{"comment":"The reference list includes several entries that are not clearly relevant to Telegram scraping (e.g., Aduma & Ntaka on social media and academic performance); the connection to the paper's topic should be clarified.","section":"§5 References"},{"comment":"Section 2 states that Google Colab 'typically crashes after running this code for around 6 hours and 20 minutes' and that the Telegram API imposes a softban after about 200 channels; these are useful practical caveats, but they are presented as tips rather than as limitations of the tool, which may overstate the 'scalability' claimed in the abstract.","section":"§2"},{"comment":"The code's `current_max_id = min(c_index + message.id, max_t_index)` is printed as a count of 'contents' but computes a sum of a channel-specific message ID and a session counter, which has no clear interpretation.","section":"§3 Table 03"}],"recommendation":"major_revision","confidential_remarks":"The paper relies heavily on self-citations to demonstrate impact, including arXiv preprints and technical notes. While this is not a circularity problem for the code's correctness, an editor may wish to consider whether the article's novelty and contribution are sufficient for the journal's scope. The absence of any evaluation or reproducible verification is a more serious concern than the self-citation pattern."},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","letter":"Quick take: this is a white paper for a Telegram scraping wrapper, not a research result. The writing is clear, the author is upfront about operational limitations like Colab timeouts and API soft bans, and the GitHub link is real. The list of downstream studies and notes shows the author has used some scraper in serious empirical work. But the code as printed cannot authenticate. In §3, Table 03 opens `TelegramClient` and immediately calls `iter_messages`, with no `client.start()` or `sign_in()` anywhere. A fresh Colab runtime has no `.session` file, so the first API call raises `UnauthorizedError`. The §2 text says Telegram may request a verification code, but there is no code path that handles it. This is a load-bearing failure, not a cosmetic typo. A user who follows the instructions gets zero messages.\n\nThe progress formula is also unreliable: `current_progress = t_index/(t_index+message_id)` has no clear relationship to the fraction of work done and will misreport completion. There are no tests, benchmarks, or comparisons against existing scrapers, and the paper's evidence of the tool's impact is mostly self-citation. That is not automatically a flaw, but here it is the only evidence for the 'robust and versatile' claim.\n\nWhat the paper does well: the step-by-step setup is understandable, the parameter explanations are clear, and the Excel/Parquet output choice is sensible. If the authentication bug were fixed and the code actually tested and pinned to a working environment, this could be a decent convenience for social scientists who already use Telethon. As it stands, the core functionality does not work from the description, so the central claim is unsupported.\n\nFor a reader deciding what to do with this: skip the white paper and go straight to Telethon docs. The paper needs a corrected and tested code listing before a referee should spend time on it. I would desk-reject it in its current form; the author can resubmit a fixed version.","headline":"Useful manual, but the printed code omits the login step, so the tool as described fails before scraping anything.","tokens_in":23913,"tokens_out":2847,"would_cite":false,"duration_ms":25328,"reading_group":"no","serious_thinker":"yes","would_accept_peer_review":false},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"deepseek-v4-flash","headline":"This paper presents TelegramScrap, an open-source Google Colab notebook that extracts messages, comments, and engagement metadata from public Telegram channels into Excel or Parquet, and claims it is a robust, versatile tool for…","keywords":["Telegram scraping","data collection tool","Telethon","Google Colab","computational social science","disinformation research","open-source software","social media analysis"],"falsifier":"Run the three cells exactly as printed in a fresh Google Colab session against a small public channel and verify that the downloaded Excel file contains the expected messages, comments, views, shares, and reactions with no errors.","tokens_in":23046,"feed_emoji":"📊","tokens_out":7116,"duration_ms":50720,"temperature":0.7,"pith_summary":"This white paper presents TelegramScrap, a three-cell Google Colab notebook that scrapes messages, comments, and metadata from public Telegram channels using the Telethon library. The author's claim is that this is a robust, versatile, and ethical solution for researchers who need structured Telegram data, and the paper supports that claim by cataloguing studies, technical notes, and legal investigations that used the tool. A sympathetic reader would care because the tool is offered as a reusable open-source pipeline that lowers the technical barrier for studying disinformation, political communication, and online communities.","feed_headline":"TelegramScrap turns public channels into Excel or Parquet data","feed_subtitle":"Pulls messages, comments, and engagement metadata from public Telegram channels into Excel or Parquet.","key_machinery":"The load-bearing component is the third cell's asynchronous loop, built on Telethon's TelegramClient, which calls iter_messages to page through each channel's history, filters by date and keyword, and fetches comments with reply_to lookups. Three named helpers -- remove_unsupported_characters, format_time, and print_progress -- clean text, present elapsed and remaining time, and show completion percentage. A running message counter triggers automatic Excel or Parquet backups every 1,000 messages, and the loop exits when the message cap or the Colab session limit is reached.","core_discovery":"The central claim is that TelegramScrap delivers a complete, customizable data-collection pipeline: after a one-time credential setup, users configure channels, a UTC date range, an optional keyword, a message cap, a timeout, and an output format (Excel or Parquet), and the notebook's asynchronous loop captures message text, views, shares, reactions, media presence, and nested comments. The paper states that the tool tracks progress in real time, writes backup files every 1,000 messages, and respects Telegram's terms of service and privacy regulations, and that its open-source release invites adaptation by the academic community.","pith_inferences":["The paper's reliance on Google Colab's six-hour sessions implies the same code could run on any always-on server with longer collection windows and fewer batching constraints, a path the author leaves implicit.","The demonstrated applications concentrate on Portuguese-language political and conspiracy communities; whether the tool performs equally well on other languages and platform structures is untested in the paper.","A natural, testable extension would compare TelegramScrap's output with Telegram's official API for the same channel to quantify completeness and fidelity of comments, reactions, and shares."],"forward_implications":["Researchers can reproduce published Telegram-based studies by running the same code, making prior analyses of disinformation and political discourse auditable.","The tool's Excel/Parquet outputs, with comments stored as JSON, provide a standard format for longitudinal and cross-corpus comparisons.","The documented soft-ban and session-timeout guidance gives practitioners a practical recipe for collecting large volumes without data loss.","Because the tool has already supported a parliamentary inquiry and cybercrime investigations, its open-source release extends those capabilities to other jurisdictions."],"supporting_citations":[{"why":"First cited application, showing the tool's use in comparing Bolsonaro's voting-machine discourse with public debate.","marker":"(Silva & Oliveira, 2023)"},{"why":"Application to institutional denialism and the early-treatment agenda during the COVID-19 pandemic in Brazil.","marker":"(Silva, 2023b)"},{"why":"Application to time-series analysis of disinformation in conspiracy theory communities.","marker":"(Rocha, Silva & Mielli, 2024)"},{"why":"Application to comparing thematic agendas among conspiracy communities via topic modeling.","marker":"(Silva & Máximo, 2024)"},{"why":"Documents the tool's data as instrumental in substantiating subpoenas in the January 8, 2023 parliamentary inquiry.","marker":"(Senado Federal, 2023)"},{"why":"Reports the tool's use in tracking cybercriminals and analyzing data leaks in Italy.","marker":"(RedHotCyber, 2024)"}],"fun_headline_variants":["Scrape public Telegram channels into Excel or Parquet","TelegramScrap: customizable data pipeline for Telegram analytics","Open-source scraper turns Telegram chats into structured datasets","Pull Telegram messages, views, and reactions into Parquet","Flexible Telegram scraping tool with real-time progress tracking"],"cache_read_input_tokens":3200,"weakest_assumption_plain":"The load-bearing premise is that the code as printed in Section 3 runs without modification in a Google Colab notebook and successfully authenticates with Telegram's API.","fun_headline_variants_meta":{"raw":{"variants":["Scrape public Telegram channels into Excel or Parquet","TelegramScrap: customizable data pipeline for Telegram analytics","Open-source scraper turns Telegram chats into structured datasets","Pull Telegram messages, views, and reactions into Parquet","Flexible Telegram scraping tool with real-time progress tracking"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.000222,"raw_usage":{"total_tokens":1417,"prompt_tokens":871,"completion_tokens":546,"prompt_tokens_details":{"cached_tokens":384},"prompt_cache_hit_tokens":384,"prompt_cache_miss_tokens":487,"completion_tokens_details":{"reasoning_tokens":468}},"tokens_in":487,"tokens_out":546,"duration_ms":4386,"temperature":1.0,"reasoning_tokens":468,"cache_read_input_tokens":384,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-11T10:14:22.417222+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"Run the three cells exactly as printed in a fresh Google Colab session against a small public channel and verify that the downloaded Excel file contains the expected messages, comments, views, shares, and reactions with no errors.","supporting_citations":[],"review_version":1}