On the evening of Turkey's most significant elections of the past two decades, Can Semercioğlu went to bed early. For the past seven years, Semercioğlu has worked for Teyit, the largest independent fact-checking group in Turkey, but that Sunday, May 14, was surprisingly one of the quietest nights he remembers at the organization. Before the vote, opinion polls had suggested that incumbent president Recep Tayyip Erdoğan was losing support due to devastating earthquakes in southeastern Turkey that killed nearly 60,000 people and a struggling economy. However, he still managed to secure just under 50 percent of the vote. His main opponent, Kemal Kılıçdaroğlu, who heads the Millet Alliance group of opposition parties, received around 45 percent, meaning the two will face off in a second round scheduled for May 28. "That night we didn't have much work to do because people were talking about the results," Semercioğlu says.

Due to this complexity, research into online hate The rapid integration of the Internet into our daily lives has led to is fragmented throughout numerous disciplines. Despite all these many benefits but also to a number of new, wide-spread threats extensive approaches and methods proposed to analyse online hate such as online hate, trolling, bullying, and generally aggressive [1, 12], limited research has investigated how hateful behaviours behaviours. While research has traditionally explored online hate, and content compare and relate across different online platforms in particular, on one platform, the reality is that such hate is a [8]. It has only recently been recognised within academic literature phenomenon that often makes use of multiple online networks. In that online hate is not simply an issue for a select few platforms, this article, we seek to advance the discussion into online hate by rather networks of hate are often linked across these platforms, harnessing a comparative approach, where we make use of various forming a global'network of networks' dynamic [6]. Natural Language Processing (NLP) techniques to computationally Our study applies various computational methods, including analyse hateful content from Reddit and 4chan relating to the 2020 topic modelling and sentiment analysis, to explore the type of US Presidential Elections. Our findings show how content and content that is promoted on Reddit and 4chan to provide unique posting activity can differ depending on the platform being used.

Two other methods to reduce dimensions are stemming and lemmatization. I will use one of the articles in the previous post. First, I create new string called news. After than I split the words to tokens. To see the effect we can compare the first 50 words of the lists.

I consider how to represent and reason with users' preferences. While areas of economics like social choice and game theory have traditionally considered such topics, I will argue that computer science and artificial intelligence bring some fresh perspectives to the study of representing and reasoning with preferences. For instance, I consider how we can elicit preferences efficiently and effectively. With one agent, the agent's desired goal may not be feasible. The agent wants a cheap, low-mileage Ferrari, but no such car exists.

Such false-name manipulations have traditionally not been considered in the theory of mechanism design. In this article, we review recent efforts to extend the theory to address this. Because some of these results are very negative, we also discuss alternative models that allow us to circumvent some of these negative results. Some of the most exciting applications of this involve making decisions based on the agents' preferences (for a more detailed discussion, see Conitzer [2010]). For example, in electronic commerce, agents can bid on items in online auctions.

AI's War on Manipulation: Are We Winning? The next day was going to be a big day: Citizens of Bitotia would once and for all establish which byte order was better, big-endian (B) or little-endian (L). Little Bit Timmy was a big supporter of little endian because that would give him the best position in the word. However, the population was split quite evenly between L and B, with a small minority of Bits who still remembered the single-tape Turing machine and preferred unary encoding (U), without any of this endianness business. Nonetheless, about half of the Bits preferred big-endian (B L U), and about half were the other way round (L B U).

The annual election for AAAI offices has taken place (15 June 1981 was the closing date for the receipt of votes) The people listed below have been elected by the membership of the AAAI to the offices as indicated. The election was special in several ways, in order to complete the initialization of officers and periods of tenure. Both a president (for 1981-82) and a president-elect (who will serve as president for 1982-83) were elected. Normally only a president-elect would be on the ballot, however, no presidentelect was elected at the last election. Twelve (12) councilors were elected, constituting a full complement of elected councilors.

We study the computational complexity of candidate control in elections with few voters, that is, we consider the parameterized complexity of candidate control in elections with respect to the number of voters as a parameter. We consider both the standard scenario of adding and deleting candidates, where one asks whether a given candidate can become a winner (or, in the destructive case, can be precluded from winning) by adding or deleting few candidates, as well as a combinatorial scenario where adding/deleting a candidate automatically means adding or deleting a whole group of candidates. Considering several fundamental voting rules, our results show that the parameterized complexity of candidate control, with the number of voters as the parameter, is much more varied than in the setting with many voters.

In 2016, restless tech-industry forecasters enjoyed a rare moment of consensus: Whatever else might be coming next, everyone seemed to agree that bots would be a big part of it. The analyst Benedict Evans, in a representative essay, located a promising future specifically in chat bots -- conversational interfaces for artificial intelligence, designed to assist with particular tasks. Facebook, the year before, created a personal-assistant chat bot, and the company would soon open its Messenger app up to outside developers, who it hoped would create more bots to help people shop, look things up or otherwise organize their lives. Amazon's Echo, by then already a surprise mainstream success, provided a tailwind: Here was a widely used artificial intelligence just sitting there on millions of countertops. These predictions were self-interested, of course.

The UK election this Thursday will be shaped by artificial intelligence. Artificial intelligence is being used to fake vocal political support on social media in the run up to the UK election. Then there's social media targeting. Huge swathes of marginalised people could be empowered by automated translation tools.