Marcolino, Leandro Soriano (University of Southern California) | Lakshminarayanan, Aravind (Indian Institute of Technology, Madras) | Yadav, Amulya (University of Southern California) | Tambe, Milind (University of Southern California)
Influence Maximization is an active topic, but it was always assumed full knowledge of the social network graph. However, the graph may actually be unknown beforehand. For example, when selecting a subset of a homeless population to attend interventions concerning health, we deal with a network that is not fully known. Hence, we introduce the novel problem of simultaneously influencing and mapping (i.e., learning) the graph. We study a class of algorithms, where we show that: (i) traditional algorithms may have arbitrarily low performance; (ii) we can effectively influence and map when the independence of objectives hypothesis holds; (iii) when it does not hold, the upper bound for the influence loss converges to 0. We run extensive experiments over four real-life social networks, where we study two alternative models, and obtain significantly better results in both than traditional approaches.
Building 8, which was created at last year's F8, has been working on a "brain-computer interface" for several months, Ms. Dugan said. Recent job postings for Building 8 show the unit is hiring engineers for a two-year project "focused on developing advanced (brain-computer interface) technologies." Ultimately, the mind-reading technology could help people type 100 words a minute from their minds--about five times faster than we type from our smartphones, Ms. Dugan told developers at the conference in San Jose, Calif. Separately, Building 8 also is working on technology that could help people "hear" with their skin, Ms. Dugan said. Building 8 tackles Facebook's bleeding edge ideas--way beyond projects such as the augmented reality technology CEO Mark Zuckerberg announced Tuesday.
Data scientists must always remember that data sets are not objective - they are selected, collected, filtered, structured and analyzed by human design. Naked and hidden biases in selecting, collecting, structuring and analyzing data present serious risks. For example, a recent Wall Street Journal article entitled "Tweets Provide New Way to Gauge TV Audiences" provides evidence of a disconnect between mainstream viewers and folks who use Twitter. The chart above shows the disconnect between the most popular and most tweeted shows - the most tweeted show is not a top ten show. While Twitter data can be useful for detecting trends and sentiments for certain areas (e.g., disease surveillance, natural disaster surveillance, product sentiments, financial trading, politics) in limited circumstances using scientific methods, it can also mislead and present a false view of reality.
The hype surrounding Lin-Manuel Miranda's off-Broadway musical was just reaching the Left Coast, but Raver-Lampman didn't know much. The production was adding two cast members to the ensemble for its Broadway debut in August, her agent told her. Would she like to audition? "I was like, 'Sure, I don't have a job,'" recalls Raver-Lampman, sitting in the plush cocktail lounge Dirty Water, on the ground floor of Twitter's San Francisco headquarters on Market Street. Hers is a story of happy accidents.
During the past decade, Twitter rendered the "pound sign" obsolete and made the "hashtag" part of our vernacular. The hashtag's uses range from sarcasm and trolling to awareness of social causes. The latter usage has been instrumental in the transition of movements from online to the real world. In honor of Twitter's 1oth birthday, here are the 10 most influential hashtags around social causes, ranked by the number of times they've been used since their inception. All numbers have been provided by Twitter.