When my parents first joined Facebook to stalk me, I thought the social network was going to become uncool and fade away like Myspace, Friendster, and the other social networks that came and went before it. Since then, we've found out that Russian spies have used it to influence American elections, that a shady British marketing firm harvested the personal data of 50 million Americans to target voters with political ads, that Facebook researchers devised an experiment to see if they could make us depressed, and the UN has claimed it played a role in genocide. In fact, my colleague Daniel Oberhaus quit, and wrote a guide on how do it if you want to do the same. But we also understand if that many people want or have to stay on Facebook to do their job or stay in touch with their family. And, after all, quitting Facebook is the ultimate first world privilege.

After revelations that political consulting firm Cambridge Analytica allegedly appropriated Facebook user data to advise Donald Trump's 2016 U.S. presidential campaign, many are calling for greater regulation of social media networks, saying a'massive data breach' has occurred. The idea that governments can regulate their way into protecting citizen privacy is appealing, but I believe it misses the mark. What happened with Cambridge Analytica wasn't a breach or a leak. It was a wild violation of academic research ethics. The CEO finally broke his silence on the misuse of 51 million users' data Wednesday evening, outlining three steps the firm plans to take to prevent something like this from happening again.

When Facebook co-founder Mark Zuckerberg posted a status update Wednesday on the still-unfolding Cambridge Analytica scandal, he called it an "issue," a "mistake" and a "breach of trust." But he didn't say it was a data breach. Ever since the news broke this weekend that the U.K. firm Cambridge Analytica obtained information about 50 million Facebook users without their knowledge, the social media site has been carefully avoiding using those words. Executives are profusely apologizing but stopping short of characterizing the situation as a data breach -- a phrase that brings to mind images of hacker frantically typing in a dark room or stolen credit card numbers being shared online. Facebook has 1.4 billion daily users it doesn't want to scare off with the "data breach" characterization. Here's what happened: A few years ago, a researcher put together a Facebook personality quiz that asked participants to download an app and give him access to their friends' data.

Facebook has been hard to miss this week as it struggles to cope with an unfolding scandal over the way data analysis firm Cambridge Analytica got hold of information about 50 million users. In the wake of the furore, Facebook has promised to take a tougher line with apps and others who want to mine the mountain of data the social network has stockpiled about its two billion active users. For some, however, this latest data gathering debacle is the final straw and the hashtag #DeleteFacebook has been trending on Twitter. The tag was used more than 50,000 times on Tuesday and Wednesday in tweets, reported the New York Times, which suggests it was a popular topic of discussion. But even if every one of those who used the tag, including singer Cher, deleted their account it would not make much difference to the social network's total population.

The scandal that has erupted around Cambridge Analytica's alleged use of 50 million Facebook profiles in the 2016 US election campaign is worrying for legitimate researchers who use social media data. Cambridge Analytica has denied any wrongdoing and said that the business tactics it used are widespread among other firms. But a day after the scandal hit, Facebook's shares plummeted on Wall Street amid a privacy backlash. The worry is the incident could also affect legitimate academic research. Social media data is a rich source of information for many areas of research in psychology, technology, business and humanities.

Newsbeat's De'Graft Mensah took the plunge and let an online security expert comb through his easily-downloadable Facebook data.

Newsbeat's De'Graft Mensah took the plunge and let an online security expert comb through his easily-downloadable Facebook data.

Facebook revenues soared to billions of pounds after it started giving away users' details. The social media giant practically doubled its takings every year after opening up profiles to'tens of thousands' of app developers. Facebook users were yesterday waking up to how much private information has been handed out. During the data gold-rush – which lasted from 2009 to 2015 – it appears almost anyone who described themselves as a'developer' could freely mine Facebook's database. Facebook revenues soared to billions of pounds after it started giving away users' details In this period, the technology firm's revenues rose sharply, from £500million in 2009 to nearly £13billion by 2015.

Aleksandr Kogan, the Russian-American academic at the center of the Cambridge Analytica controversy, has been relatively vocal yet strikingly calm in public since news broke that the company had acquired personal information from more than 50 million Facebook accounts and later used it to boost Donald Trump's presidential campaign. Cambridge Analytica and Facebook have both blamed Kogan for the imbroglio, though he's denied any wrongdoing. In Facebook's first statement disclosing Cambridge Analytica's activities on Friday, the company accused Kogan of lying and breaking its policies. According to its narrative, Kogan gave data he had gathered from users and their friends through his personality prediction Facebook app to the political consulting firm. Developers are not allowed to give such data to third parties.

Motivated by Supervised Opinion Analysis, we propose a novel framework devoted to Structured Output Learning with Abstention (SOLA). The structure prediction model is able to abstain from predicting some labels in the structured output at a cost chosen by the user in a flexible way. For that purpose, we decompose the problem into the learning of a pair of predictors, one devoted to structured abstention and the other, to structured output prediction. To compare fully labeled training data with predictions potentially containing abstentions, we define a wide class of asymmetric abstention-aware losses. Learning is achieved by surrogate regression in an appropriate feature space while prediction with abstention is performed by solving a new pre-image problem. Thus, SOLA extends recent ideas about Structured Output Prediction via surrogate problems and calibration theory and enjoys statistical guarantees on the resulting excess risk. Instantiated on a hierarchical abstention-aware loss, SOLA is shown to be relevant for fine-grained opinion mining and gives state-of-the-art results on this task. Moreover, the abstention-aware representations can be used to competitively predict user-review ratings based on a sentence-level opinion predictor.