TL00002 Ad Observer
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Summary: eWitness uses blockchain to establish provenance of images and videos taken from a smart phone camera. eWitness can be used to gather evidence of crime, human rights violation, domestic violence, corruption, traffic violations and more. An eWitness user is protected behind a pseudo-identity which is hidden even from the eWitness backend, until the user is ready to reveal themselves or to quietly pass the evidence on to their case-worker, trusted friend or sponsor. The purpose of eWitness is to create images and videos that can be trusted. The technology behind eWitness, provides the proof of location and time the media was taken and the proof that the media was not altered to misinform or deform the fact in any manner. - Details
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Summary: Browse the internet, and find a Tweet, YouTube video, Facebook post or an Instagram photo that you want your team to fact-check and investigate. Click the Check icon, choose one of the projects you want to add this item to, and save it - Details
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Summary: Emergent is a real-time rumor tracker. It's part of a research project with the Tow Center for Digital Journalism at Columbia University that focuses on how unverified information and rumor are reported in the media. It aims to develop best practices for debunking misinformation. - Details
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Summary: Browser extension designed to promote awareness when reading news on Twitter. This browser extension changes twitter feed by making posts more or less visible and help users distinguish news with differing level of reliability. - Details
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Summary: The aim of GLTR is to take the same models that are used to generated fake text as a tool for detection. GLTR has access to the GPT-2 117M language model from OpenAI, one of the largest publicly available models. It can use any textual input and analyze what GPT-2 would have predicted at each position. Since the output is a ranking of all of the words that the model knows, we can compute how the observed following word ranks. We use this positional information to overlay a colored mask over the text that corresponds to the position in the ranking. A word that ranks within the most likely words is highlighted in green (top 10), yellow (top 100), red (top 1,000), and the rest of the words in purple. Thus, we can get a direct visual indication of how likely each word was under the model. - Details
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Summary: scrapes Facebook, Twitter, Reddit, Youtube, Pinterest, Tumblr APIs - Details
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