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Cryptographic tools for authenticating the provenance of web-based information are a promising approach to increasing trust in online news and information. However, making these tools’ technical assurances sufficiently usable for news consumers is essential to realizing their potential. We conduct an online study with 160 participants to investigate how the presentation (visual vs. textual) and location (on a news article page or a third-party site) of the provenance information affects news consumers’ perception of the content’s credibility and trustworthiness, as well as the usability of the tool itself. We find that although the visual presentation of provenance information is more challenging to adopt than its text-based counterpart, this approach leads its users to put more faith in the credibility and trustworthiness of digital news, especially when situated internally to the news article. 

Previous studies, both in psychology and linguistics, have shown that individuals with mental illnesses show deviations from normal language use, that these differences can be used to make predictions, and used as a diagnostic tool. Recent studies have shown that machine learning can be used to predict people with mental illnesses based on their writing. However, little attention is paid to the interpretability of the machine learning models. In this talk we will describe our analysis of the machine learning models, the different language patterns that distinguish individuals having mental illnesses from a control group, and the associated privacy concerns. We use a dataset of Tweets that are collected from users who reported a diagnosis of a mental illnesses on Twitter. Given the self-reported nature of the dataset, it is possible that some of these individuals are actively talking about their mental illness on social media. We investigated if the machine learning models are detecting the active mentions of the mental illness or if they are detecting more complex language patterns. We then conducted a feature analysis by creating feature vectors using word unigrams, part of speech tags and word clusters and used feature importance measures and statistical methods to identify important features. This analysis serves two purposes: to understand the machine learning model, and to discover language patterns that would help in identifying people with mental illnesses. Finally, we conducted a qualitative analysis of the misclassifications to understand the potential causes for the misclassifications.