**Ambition:** Texture Oracle will use Twitter data to (1) predict COVID-19 outbreaks in UK postcodes and (2) evaluate the effectiveness of public health messaging designed to prevent or tackle outbreaks. Academic research shows that disease outbreaks can be predicted by way of patterns in language use; we will use team members' original research to improve existing methods and apply them to COVID-19\. Similarly, advances in AI and natural language processing make it possible to establish a message's 'echo' in social media: this will allow us to grade the effectiveness of public health messaging. Oracle will therefore complement biomedical and public health responses to COVID-19 with an independent layer of predictive and evaluative capacity based on language data. **Predicting COVID-19:** There are two ways in which we aim to predict the increased likelihood of a COVID-19 outbreak in an area: * _Tracking risky behaviour_: COVID-19 is more likely to occur when people fail to observe social distancing and quarantine measures. Typically, people responsible for this kind of risk-taking experience future rewards as less valuable than present rewards; thus, avoiding infection is felt as less valuable than near-term gratification. The tendency to devalue future rewards can be inferred from how a piece of language refers to the future: typically, it is represented as being less certain. Using AI methods developed by project members, we will measure this tendency in region-specific tweets and use it to predict COVID-19 outbreaks * _Tracking symptoms_: Influenza outbreaks can be predicted by tracking symptom mentions in tweets. Research by project members extends this method by providing a new resource for tracking symptom expression. The Lancaster sensorimotor norms classify 40k English words for the extent to which they evoke six perceptual modalities (touch, hearing, smell, taste, vision, and feelings inside the body (interoception)) and five action effectors (mouth/throat, hand/arm, foot/leg, head (excluding mouth/throat), and torso). This creates a much stronger link between everyday language and bodily states than symptom terms alone, allowing us to use the sensorimotor profile of tweets to predict outbreaks. **Evaluating public-health messaging:** Government messaging causes priming effects, meaning that effective messages are reproduced in people's terminology and language structure. We will use AI methods to extract these 'echoes' in tweets, allowing us identify how well public health messages have penetrated on a regional basis. Doing this will give public health agencies the information needed to target (and re-target) their communications to areas most at risk.