@misc{schulz_covid_2022,
	title = {{COVID} {Fake} {News} {Detector}},
	url = {https://live.european-language-grid.eu/catalogue/tool-service/18690},
	abstract = {{\textless}p{\textgreater}This service detects Fake News in a German text about COVID-19. It uses a {\textless}a href="https://huggingface.co/bert-base-german-cased" target="\_blank"{\textgreater}German BERT model{\textless}/a{\textgreater} as binary text classifier. The result is given as a probability between 0 and 1: How likely is the information in that text to be reliable, without any Fake News?{\textless}/p{\textgreater}{\textless}p{\textgreater}The model was trained on the {\textless}a href="https://github.com/justusmattern/fang-covid" target="\_blank"{\textgreater}FANG-COVID dataset{\textless}/a{\textgreater}. The dataset contains 41,242 documents labeled as either real (68\%) or fake (32\%). The ground truth was derived from automatic annotation based on the publication platform of a text (newspapers, websites, etc.). The publication platforms were associated with global labels (real or fake) as introduced by independent organizations such as Correctiv or NewsGuard.{\textless}/p{\textgreater}},
	urldate = {2022-06-22},
	publisher = {Deutsches Forschungszentrum für Künstliche Intelligenz},
	author = {Schulz, Konstantin},
	month = may,
	year = {2022},
}

{"_id":"hpLjCz4eiSPrQgGZn","bibbaseid":"schulz-covidfakenewsdetector-2022","author_short":["Schulz, K."],"bibdata":{"bibtype":"misc","type":"misc","title":"COVID Fake News Detector","url":"https://live.european-language-grid.eu/catalogue/tool-service/18690","abstract":"\\textlessp\\textgreaterThis service detects Fake News in a German text about COVID-19. It uses a \\textlessa href=\"https://huggingface.co/bert-base-german-cased\" target=\"_blank\"\\textgreaterGerman BERT model\\textless/a\\textgreater as binary text classifier. The result is given as a probability between 0 and 1: How likely is the information in that text to be reliable, without any Fake News?\\textless/p\\textgreater\\textlessp\\textgreaterThe model was trained on the \\textlessa href=\"https://github.com/justusmattern/fang-covid\" target=\"_blank\"\\textgreaterFANG-COVID dataset\\textless/a\\textgreater. The dataset contains 41,242 documents labeled as either real (68%) or fake (32%). The ground truth was derived from automatic annotation based on the publication platform of a text (newspapers, websites, etc.). The publication platforms were associated with global labels (real or fake) as introduced by independent organizations such as Correctiv or NewsGuard.\\textless/p\\textgreater","urldate":"2022-06-22","publisher":"Deutsches Forschungszentrum für Künstliche Intelligenz","author":[{"propositions":[],"lastnames":["Schulz"],"firstnames":["Konstantin"],"suffixes":[]}],"month":"May","year":"2022","bibtex":"@misc{schulz_covid_2022,\n\ttitle = {{COVID} {Fake} {News} {Detector}},\n\turl = {https://live.european-language-grid.eu/catalogue/tool-service/18690},\n\tabstract = {{\\textless}p{\\textgreater}This service detects Fake News in a German text about COVID-19. It uses a {\\textless}a href=\"https://huggingface.co/bert-base-german-cased\" target=\"\\_blank\"{\\textgreater}German BERT model{\\textless}/a{\\textgreater} as binary text classifier. The result is given as a probability between 0 and 1: How likely is the information in that text to be reliable, without any Fake News?{\\textless}/p{\\textgreater}{\\textless}p{\\textgreater}The model was trained on the {\\textless}a href=\"https://github.com/justusmattern/fang-covid\" target=\"\\_blank\"{\\textgreater}FANG-COVID dataset{\\textless}/a{\\textgreater}. The dataset contains 41,242 documents labeled as either real (68\\%) or fake (32\\%). The ground truth was derived from automatic annotation based on the publication platform of a text (newspapers, websites, etc.). The publication platforms were associated with global labels (real or fake) as introduced by independent organizations such as Correctiv or NewsGuard.{\\textless}/p{\\textgreater}},\n\turldate = {2022-06-22},\n\tpublisher = {Deutsches Forschungszentrum für Künstliche Intelligenz},\n\tauthor = {Schulz, Konstantin},\n\tmonth = may,\n\tyear = {2022},\n}\n\n","author_short":["Schulz, K."],"key":"schulz_covid_2022","id":"schulz_covid_2022","bibbaseid":"schulz-covidfakenewsdetector-2022","role":"author","urls":{"Paper":"https://live.european-language-grid.eu/catalogue/tool-service/18690"},"metadata":{"authorlinks":{}}},"bibtype":"misc","biburl":"https://api.zotero.org/users/912485/collections/VL2FMYT9/items?key=tJHJRX0dfdnwe3iuxbtyH4ht&format=bibtex&limit=100","dataSources":["nsAxma8Gv9c6fai5n"],"keywords":[],"search_terms":["covid","fake","news","detector","schulz"],"title":"COVID Fake News Detector","year":2022}