As I scan my Netflix page, which rectangular tiles leap to the forefront of my attention? There's Sexify, with its promotional photo of a woman's face seemingly captured at the peak of passion. Or maybe I should watch Lucifer, its star staring shirtlessly into my soul, his chest so uncannily hairless it looks like a video game character's. The answer to What Lies Below might be a jacked aquatic geneticist with a Superman jaw line, but other mysteries are left teasingly unsolved by Netflix's most popular titles. For a while this spring, Why Did You Kill Me? jostled for space on the Top 10 list with Who Killed Sara?

Social Media has influenced the way people socially connect, interact and opinionize. The growth in technology has enhanced communication and dissemination of information. Unfortunately,many terror groups like jihadist communities have started consolidating a virtual community online for various purposes such as recruitment, online donations, targeting youth online and spread of extremist ideologies. Everyday a large number of articles, tweets, posts, posters, blogs, comments, views and news are posted online without a check which in turn imposes a threat to the security of any nation. However, different agencies are working on getting down this radical content from various online social media platforms. The aim of our paper is to utilise deep learning algorithm in detection of radicalization contrary to the existing works based on machine learning algorithms. An LSTM based feed forward neural network is employed to detect radical content. We collected total 61601 records from various online sources constituting news, articles and blogs. These records are annotated by domain experts into three categories: Radical(R), Non-Radical (NR) and Irrelevant(I) which are further applied to LSTM based network to classify radical content. A precision of 85.9% has been achieved with the proposed approach

IBM has started to use its artificial intelligence programme, Watson, to plan and buy media in the UK. It's something the tech giant has been doing in the US for over 18 months and has thus far delivered massive gains in the performance of its online ad campaigns, according to the business. Sitting within The Trade Desk – its media planning platform – Watson over time learns how effectively a campaign is performing for different audiences at different times, locations, devices and browser. Based on this information, it will then only bid on inventory that aligns to any given audience, and even then will consider the size of an ad and how effective it will be in relation to those other factors. In the US, IBM claims this has reduced its cost per click by as much as 71%, although the average hovers around the 31% mark.

As doubts about vaccine safety threaten adequate coverage across the world, surveillance of online media, a widespread source of information about vaccines, can prove useful in many regards. The Vaccine Sentimeter, an extension of HealthMap, was established for the purpose of monitoring online vaccine discussion in news, blogs, and other websites. The development of the tool involved extensive manual annotation of media content, such as the sentiment or category of discussion in the report. Given its relevance to vaccine confidence and public health, the current paper describes in detail the annotated discussion of adverse event following immunization (AEFI). The goal of the current project is to automate the detection of AEFI reports in the media. Future steps in this automation process are proposed.