Our goal is to present how we use the “huge data” collected by using open APIs of Twitter and other online services to empirically test and improve existing models in social sciences such as economics and sociology. Bringing together natural language processing and macroeconomics, on top of the troves of machine-generated data, we also propose building innovative applications to track consumer sentiment and industry dynamics.
by Rion Snow
Twitter is redefining the way information is reported, spread, interacted with, and absorbed. Each individual on Twitter can fluidly act as a primary source, a filter, an information catalyst, and a consumer. Taken collectively, the information preferences expressed by Twitter users provide a valuable signal indicating the relevance of information and information sources across Twitter and the web as a whole. In this talk we consider the information ecosystem of Twitter through the lenses of lists, top tweets, and trending topics, exploring the emergence and value of transparently communicated information preferences.
by Rob LaGesse
Too many customers are sitting listening to hold music waiting for their problem to get resolved. Instead of stewing privately they are now airing their grievances publicly. To anyone and everyone that will listen. The BP oil spill and Toyota recalls have showed us how people are using social media tools to give pissed off customers a new voice – and it’s a megaphone. Knowing your customer and understanding how to address everything from a crisis to the everyday question quickly and effectively is critical.
Learn about some of the biggest flubs from 2010, how the ball was dropped and what could have been done differently. Don’t make the same mistakes they did. Learn how not to mess up.
by Liangfei Qiu
Information aggregation mechanisms are designed explicitly for collecting and aggregating dispersed information. A best example of utilizing such kind of "the wisdom of crowds" is prediction market. The purpose of our project is to suggest that carefully designed market mechanisms can elicit dispersed information, which will improve our prediction. In a prediction market, payoffs are tied to the outcomes of future events and one typically trades a security that pays $1 if a specified event occurs. Generally speaking, participants are compensated for accuracy in forecasting. Many business examples share the following characteristic: small bits and pieces of relevant information exists in the opinions and intuition of diverse individuals. The prediction markets will produce reliable forecasts about sales, financial and accounting results by gathering small pieces of individual information. The development of the Internet provides us with a twitter based technology to design prediction markets. The information propagation in twitter community is a complex social network, and will improve people's predictions in prediction markets.
by DJ Edgerton
Could you save a life in 140 characters? That was the challenge put to the development team at Zemoga, a leading interactive agency. Using the Twitter API they created Follow Me, a Twitter app that connects patients, doctors and caregivers. While many pharma and healthcare companies have grappled with how best to use social media, firms like Zemoga have taken it to the next level by focusing on the patient first. Follow Me lets disease state sufferers update physicians, family members and other caregivers on their health states as easily as tweeting about Justin Bieber or last night's baseball game. An easy to use interface let's them select an emotional or physical state and it's sent out to a private Twitter network made up of followers that have been authorized by the patient. Doctors can view all of their patients statuses through a customized dashboard and follow up with the ones who've expressed a negative emotional or physical state. They can ask questions about physical conditions, compliance with drug prescriptions, and other highly relevant and personal subjects. Family members and caregivers can also check in, monitoring conditions and sending reminders to patients about diet, compliance or other healthcare related matters. While a Follow Me demo will form the heart of the presentation, we want to encourage a discussion about how pharma/healthcare can move beyond the current "mass market" approach to patient communication and engage individuals using social media.
11th–15th March 2011