people's apparent prevalence can be amplified, stimulated or simulated. (See also: Mere-exposure effect) Information timeliness and uncorrectability: Clarifications, conspiracy busting and fake news exposure often come late when the damage is already done and/or do not reach the bulk of the audience of the associated misinformation Psychological targeting: Social media activities and other data can be used to analyze the personality of people and predict their behaviour and preferences. Dr Michal Kosinski developed such a procedure. Such can be used for media or information tailored to a person's psyche e.g. via Facebook. According to reports such may have played an integral part in Donald Trump's win. (See also: Targeted advertising, Personalized marketing) Algorithms, echo chambers and polarization Due to overabundance of online content, social networking platforms and search engines have leveraged algorithms to tailor and personalize users' feeds based on their individual preferences. However, algorithms also restrict exposure to different viewpoints and content. This is commonly referred to as "echo-chambers" and "filter-bubbles". With the help of algorithms, filter bubbles influence users' choices and perception of reality by giving the impression that a particular point of view or representation is widely shared. Following the 2016 referendum of membership of the European Union in the United Kingdom and the United States presidential elections, this gained attention as many individuals confessed their surprise at results that seemed very distant from their expectations. The range of pluralism is influenced by the personalized individualization of the services and the way it diminishes choice. Five manipulative verbal influences were found in media texts. There are self-expression, semantic speech strategies, persuasive strategies, swipe films and information manipulation. The vocabulary toolkit for speech manipulation includes euphemism, mood vocabulary, situational adjectives, slogans, verbal metaphors, etc. Research on echo chambers from Flaxman, Goel, and Rao, Pariser, and Grömping suggest that use of social media and search engines tends to increase ideological distance among individuals. Comparisons between online and off-line segregation have indicated how segregation tends to be higher in face-to-face interactions with neighbors, co-workers, or family members, and reviews of existing research have indicated how available empirical evidence does not support the most pessimistic views about polarization. A study conducted by Bakshy, Messing, and Adamic from Facebook and the University of Michigan, for example, has suggested that individuals’ own choices drive algorithmic filtering, limiting exposure to a range of content. While algorithms may not be causing polarization, they could amplify