Knowledge will Propel Machine Understanding of Content: Extrapolating from Current Examples.

Machine Learning has been a big success story during the AI resurgence. One particular stand out success relates to learning from a massive amount of data. In spite of early assertions of the unreasonable effectiveness of data, there is increasing recognition for utilizing knowledge whenever it is available or can be created purposefully. In this paper, we discuss the indispensable role of knowledge for deeper understanding of content where (i) large amounts of training data are unavailable, (ii) the objects to be recognized are complex, (e.g., implicit entities and highly subjective content), and (iii) applications need to use complementary or related data in multiple modalities/media. What brings us to the cusp of rapid progress is our ability to (a) create relevant and reliable knowledge and (b) carefully exploit knowledge to enhance ML/NLP techniques. Using diverse examples, we seek to foretell unprecedented progress in our ability for deeper understanding and exploitation of multimodal data and continued incorporation of knowledge in learning techniques.

Finding Street Gang Members on Twitter.

Most street gang members use Twitter to intimidate others, to present outrageous images and statements to the world, and to share recent illegal activities. Their tweets may thus be useful to law enforcement agencies to discover clues about recent crimes or to anticipate ones that may occur. Finding these posts, however, requires a method to discover gang member Twitter profiles. This is a challenging task since gang members represent a very small population of the 320 million Twitter users. This paper studies the problem of automatically finding gang members on Twitter. It outlines a process to curate one of the largest sets of verifiable gang member profiles that have ever been studied. A review of these profiles establishes differences in the language, images, YouTube links, and emojis gang members use compared to the rest of the Twitter population. Features from this review are used to train a series of supervised classifiers. Our classifier achieves a promising F1 score with a low false positive rate.

EmojiNet: Building a Machine Readable Sense Inventory for Emoji.

Emoji are a contemporary and extremely popular way to enhance electronic communication. Without rigid semantics attached to them, emoji symbols take on different meanings based on the context of a message. Thus, like the word sense disambiguation task in natural language processing, machines also need to disambiguate the meaning or 'sense' of an emoji. In a first step toward achieving this goal, this paper presents EmojiNet, the first machine readable sense inventory for emoji. EmojiNet is a resource enabling systems to link emoji with their context-specific meaning. It is automatically constructed by integrating multiple emoji resources with BabelNet, which is the most comprehensive multilingual sense inventory available to date. The paper discusses its construction, evaluates the automatic resource creation process, and presents a use case where EmojiNet disambiguates emoji usage in tweets. EmojiNet is available online for use at http://emojinet.knoesis.org.

"Time for dabs": Analyzing Twitter data on marijuana concentrates across the U.S.

AIMS: Media reports suggest increasing popularity of marijuana concentrates ("dabs"; "earwax"; "budder"; "shatter; "butane hash oil") that are typically vaporized and inhaled via a bong, vaporizer or electronic cigarette. However, data on the epidemiology of marijuana concentrate use remain limited. This study aims to explore Twitter data on marijuana concentrate use in the U.S. and identify differences across regions of the country with varying cannabis legalization policies. METHODS: Tweets were collected between October 20 and December 20, 2014, using Twitter's streaming API. Twitter data filtering framework was available through the eDrugTrends platform. Raw and adjusted percentages of dabs-related tweets per state were calculated. A permutation test was used to examine differences in the adjusted percentages of dabs-related tweets among U.S. states with different cannabis legalization policies. RESULTS: eDrugTrends collected a total of 125,255 tweets. Almost 22% (n=27,018) of these tweets contained identifiable state-level geolocation information. Dabs-related tweet volume for each state was adjusted using a general sample of tweets to account for different levels of overall tweeting activity for each state. Adjusted percentages of dabs-related tweets were highest in states that allowed recreational and/or medicinal cannabis use and lowest in states that have not passed medical cannabis use laws. The differences were statistically significant. CONCLUSIONS: Twitter data suggest greater popularity of dabs in the states that legalized recreational and/or medical use of cannabis. The study provides new information on the epidemiology of marijuana concentrate use and contributes to the emerging field of social media analysis for drug abuse research.