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However, as Twitter’s popularity soared, some companies experimented with utilising the Twitter ‘firehose’ (a real-time pipe of a day.

Tweets could be analysed and parsed for pertinent information, their content could be scored according to the number of retweets or the number of followers the account came from; this provided the HFT algorithms with a rich and real-time source of new data.

In concert, these trends were picked up by HFTs and other automated trading bots that sent the stock price soaring (Farooq, Khan and Khalid, 2014).

Similarly, on April 23, 2013, the Syrian Electronic Army hacked the Twitter account of the Associated Press (AP) and posted a tweet claiming that the White House had been subject to a terror attack and President Obama injured.

In harm of Bidder’s Edge (an auction aggregator site) bots were sufficiently substantial to meet the requirements for trespass to chattels.

Conversely however, a Central District of California hearing of constitute trespass to chattels as “the taking of factual information from a public source was not a trespass” (Quilter, 2002).

However, the journey from Turing’s original vision to modern social bots has been circuitous; from early web crawlers, automated financial algorithms, simple bots on social networks through to the latest, emerging social bots[1], these automated technologies have rubbed against legal grey areas spanning trespass, intellectual property, impersonation and data privacy.

Parallel to this, the advent of hyper-scale, cloud-based systems and data analysis, combined with advanced artificial intelligence techniques means the potential for this convergent technology to shake the foundations of our privacy and even legal frameworks needs to be considered.

Finally, we will look at the legal ramifications of social bots as their technology matures by examining the effects of filter bubbles, data collection, social trust and whether bots can have intent.

Since the 1950s and Alan Turing’s infamous ‘Turing Test’, the concept of intelligent machines conversing with humans has driven academic artificial intelligence research.

Some HFT algorithms were designed to exit a market if they detected sudden volatility or market activity that indicated a new phenomenon not designed within the algorithm.

As these HFTs exited the financial markets, the resultant cash crunch caused further pressure and market drops.

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