According to an ACLU blog post published on Tuesday, law enforcement officials implemented a far-reaching surveillance program to track protesters in both Ferguson, MO and Baltimore, MD during their recent uprisings and relied on special feeds of user data provided by three top social media companies: Twitter, Facebook and Instagram. Specifically, all three companies granted access to a developer tool called Geofeedia which allows users to see the geographic origin of social media posts and has been employed by more than 500 law enforcement organizations to track protesters in real time. Law enforcement's ability to monitor the online activities of protesters could have a chilling effect on First Amendment rights, the post asserts. "These platforms need to be doing more to protect the free speech rights of activists of color and stop facilitating their surveillance by police," Nicole Ozer, technology and civil liberties policy director for the ACLU of California, told the Washington Post. "The ACLU shouldn't have to tell Facebook or Twitter what their own developers are doing. The companies need to enact strong public policies and robust auditing procedures to ensure their platforms aren't being used for discriminatory surveillance."

A demonstrator is arrested during a protest marking the one-year anniversary of the shooting of Michael Brown along West Florrisant Street on Aug.10, 2015 in Ferguson, Mo. (Photo: Scott Olson, Getty Images) SAN FRANCISCO -- Police monitored and tracked protests in Baltimore and Ferguson, Mo., using data from Twitter, Facebook and Instagram provided by a Chicago company that analyzes social media for law enforcement, according to the ACLU. The data provided to police by Geofeedia included the locations of users. The ACLU said after it alerted the social media companies, Instagram cut off Geofeedia's access to public user posts and Facebook cut off its access to a topic-based stream of public user posts. Twitter suspended the company, Geofeedia, from receiving its commercial data, Twitter said in a tweet Tuesday. In an emailed statement, Facebook said Geofeedia had access to data that Facebook users make public.

If you do text analytics and sentiment analysis then you've likely come to expect the open and free APIs from all the major social media sources as something that won't go away. But about 90 days ago Facebook withdrew open access to its Facebook posts data stream and made it available only to a select list of developers that support Facebook. This is quite a blow to the larger social media monitoring industry but may be just the first of many instances where the big social media sites decide to go proprietary in order to better monetize their information asset.

A significant chunk of the data that we encounter on a daily basis is available in an unstructured, free text format. Hence, the ability to glean useful bits of information from this unstructured pile can be quite valuable. In this post, we will attempt a basic analysis of the text from the first Presidential debate between Clinton and Trump. A good part of this post involves data manipulation steps to convert the raw transcript text (of the debate) into a more structured/ ordered form, which you can then start analyzing – This initial data manipulation process to transform the raw text into a more structured form suitable for further analysis/modelling, is a key step in any text analytics effort, and hence a key focus point of this post. Post data transformation and structuring, we attempt to answer a few simple questions from the data (such as Who spoke more, Who interrupted more, Key discussion points etc).

For many new Facebook employees, their first days begin in front of a computer screen, learning the ins and outs of the company's code. That code, after all, serves as the foundation of the company's gigantic social network, hosting more than 1 billion daily visitors. With that in mind, it may be surprising to hear that Joel Kjellgren's first six months at Facebook were spent working out of a construction trailer. He doesn't work on the Like buttons, notification icons, or the other tools and buttons Facebook members push and poke on a regular basis. Rather, as the site manager of Facebook's data center in Luleå, Sweden, Kjellgren's oversees the massive facility that processes petabytes of data in the form of photos and stories posted to Facebook.

Nasa has announced that it has found evidence of flowing water on Mars. Scientists have long speculated that Recurring Slope Lineae -- or dark patches -- on Mars were made up of briny water but the new findings prove that those patches are caused by liquid water, which it has established by finding hydrated salts. Several hundred camped outside the London store in Covent Garden. The 6s will have new features like a vastly improved camera and a pressure-sensitive "3D Touch" display

Sentiment analysis is widely used to gauge public opinion towards products, to analyze customer satisfaction, and to detect trends. With the proliferation of customer reviews, more fine-grained aspect-based sentiment analysis (ABSA) has gained in popularity, as it allows aspects of a product or service to be examined in more detail. To this end, we have launched an ABSA service a while ago and demonstrated how the service can be used to gain insights into the strengths and weaknesses of a product. For performing sentiment analysis on customer reviews (as well as with many other text classification tasks), we face the problem that there are many different categories of reviews such as books, electronics, restaurants, etc. (You only need to have a look at the Departments tab on Amazon to get a feeling for the diversity of these categories.) In Machine Learning and Natural Language Processing, we refer to these different categories as domains; every domain has their unique characteristics.

Most of the systems on the market will clock anywhere around 55-65% for unseen data, even though they might be 85% accurate in their cross-validations. At this juncture, it's important to realize that sentiment analysis is critical for any system monitoring customer reviews or social media posts. Hardly had the business world caught up with a sentence level sentiment analysis, we are now moving to aspect level sentiment analysis - more directed & granular, adding to the complexity. The question is this - can we do something to augment our sentiment analysis? For the past few months, I have been using context and relationship extraction to augment sentiment analysis.

The Dirichlet process and its extension, the Pitman-Yor process, are stochastic processes that take probability distributions as a parameter. These processes can be stacked up to form a hierarchical nonparametric Bayesian model. In this article, we present efficient methods for the use of these processes in this hierarchical context, and apply them to latent variable models for text analytics. In particular, we propose a general framework for designing these Bayesian models, which are called topic models in the computer science community. We then propose a specific nonparametric Bayesian topic model for modelling text from social media. We focus on tweets (posts on Twitter) in this article due to their ease of access. We find that our nonparametric model performs better than existing parametric models in both goodness of fit and real world applications.

Nasa has announced that it has found evidence of flowing water on Mars. Scientists have long speculated that Recurring Slope Lineae -- or dark patches -- on Mars were made up of briny water but the new findings prove that those patches are caused by liquid water, which it has established by finding hydrated salts. Several hundred camped outside the London store in Covent Garden. The 6s will have new features like a vastly improved camera and a pressure-sensitive "3D Touch" display