Trust, Influence, and Noise: Implications for Safety Surveilance Bill Rand Asst. Prof. of Marketing and Computer Science Director of the Center for Complexity in Business

Data Science Data >Large and rich sources of data of all types >Social media, GIS, loyalty cards, CRM, Open-source mainstream media Science >Developing theories of how and why people interact >Hypothesis creation, First principles of consumer behavior Storytelling >Explaining the science of the data to others >Analysis, Visualization, Modeling, Simulation

Cutting through the Noise Opportunity: Social Media is a great marketing channel. Challenge: However, there is a lot of noise, and its not apparent what users we should be paying attention to for monitoring. Solution: Identify properties that are indicative of future conversations.

Influence Influential users are ones who are able to reach a lot of users quickly with their messaging. How do you identify influentials?

Trust Trust is a measure of how much one user believes the content of another user. How does trust evolve on social media? Does understanding trust help you in modeling conversations?

Different Methods for Identification Baseline – How many messages do they generate? Past Scores – How many conversations have they created before? Static – How many friends? Dynamic – What are the dynamics of conversations?

Identifying Trends on Social Media To identify trends, you need to establish a baseline, but how do you establish that baseline? What matters? – Subject – Geography – Time

Inferring Geolocation in Social Media Data Geolocation in social media can be inferred from three different types of data: – Geoencoded Data – User-described Location – Ambient Geography Ambient Geography is the use of references in natural language text to help determine the location being referenced We are developing a Bayesian modeling framework to constantly update a user’s most probable location based on their social media activity Among the many benefits, we plan to use this tool to help verify the accuracy of social media content, since the proximity of a user to an event can help assess their credibility

Challenges and Opportunities Challenges – We need better methods to automatically assess the quality and impact of social media content – The failure of Google Flu Trends indicates that the solution is not in big data analysis unguided by theory – There is a selection bias in terms of those who use social media to talk about health, we need to account for this bias Opportunities – These tools will have more resolution as we move into the future – New methods of filtering and content analysis will improve the overall results – Combining multiple signals about quality of content will improve surveillance In the end, we need to cut through the noise

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