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This paper will examine the history of these social bots, from their humble start as web crawlers and simple, action-orientated algorithms, to the more complicated self-learning bots that roam social networks such as Twitter and Facebook.Furthermore, we will look at the complications that arise from teaching bots from social data and explore whether the algorithmic filtering of content used to increase consumer engagement skews or biases a bot’s ability to interact meaningfully across a range of audiences.The journey to this point has been long and tumultuous with these new technologies causing legal questions throughout their development.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.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).

As these HFTs exited the financial markets, the resultant cash crunch caused further pressure and market drops.After a five-year investigation, it became apparent that it was several HFT algorithms that were largely responsible.HFT algorithms were involved in buying and selling the same stock—known as a “hot potato”—causing a collapse in the price of certain stocks.Automated, high-frequency trading (HFT) algorithms trade stock extremely quickly and were becoming more popular as real-time data became increasingly prevalent and the cost of zero-latency systems dropped.The faster the algorithm could trade on near-realtime information, the earlier it could make money on the data it had received.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.


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