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Social media: influence on politics and economy

Over the past few years technologies such as the internet have advanced in leaps and bounds. It is only natural that this brings new risks with it, risks that many people could have never even conceived of. Personal data like names, addresses, phone numbers – to mention only a few – have increasingly become the focus of both corporate and political interest. Recent events like the data leaks at Facebook as well as the US presidential elec-tion have drawn attention to the lack of reliable data protection laws. Furthermore, because of this, questions such as “How impressionable is the human mind?” and “Can third parties use our online profile to influence our daily lives through subterfuge?” have come up. In this paper, I endeavor to answer the following questions:  Does social media, especially advertising on social media, affect consumer behav-ior in any way?  Has the rise of social networks caused a cultural change, and if so, how did the cul-ture change?  Can political situations be influenced through big data exploitation?  How do tech giants such as Facebook generate revenue, even though it is ostensi-bly without costs for the end-user? In order to do so, I will base my arguments on a variety of research papers, which will ena-ble me to present any information in a detached and unbiased way. Given how recent both the events and the technologies used are, I find myself unable to provide the results of any long-term studies. Bla bla bla 3 INTRODUCTION 4 PERSONAL DATA ON SOCIAL NETWORKS 4.1 BIG DATA AND DATA MINING Before delving into personal data as a chapter on its own, I feel it is important to look at how the corporate world is using aforementioned data. Corporate is using many different strategies to achieve more revenue. One of them is big data analytics. In the past few years the complexity of data analytics has skyrocketed, largely due to the fact that data can be won in unprecedented amounts. Big data has sev-eral definitions. However, the widely accepted one is that it comprises of three factors: (Xiaomeng, n.d., p. 2)  Volume: As implied, data sets are quite large.  Velocity: Incredibly high refresh rate, data comes in fast and is only accurate for a short amount of time.  Variety: Data comes from many sources and in many different forms. Transac-tional log data from apps, or video, audio, text, images and similar. Social media is a big provider of the latter. Figure 1: Big Data and its aspects The most used definition reflects these three components too: Big data is high-volume, high-velocity and/or high-variety information assets that demand cost-effective, innovative forms of information processing that enable enhanced insight, decision making, and process automation. (Gartner, 2012) (Xiaomeng, n.d., p. 3) Data is nowadays captured at a high frequency. A few examples of such data include, but are not limited to:  Internet logs (page views, searches on Google, bing.com, Yahoo and similar, pur-chases), used to improve areas like customer segmentation, marketing in form of targeted advertising, other offers, etc.…  Text (emails, Facebook feeds, messages and others), extracts facts from text to use in analytics…  Smart grid and sensor data (mostly from high-tech machinery and engines such as wind turbines and pipelines), can be analyzed for insights on performance and the like  Social networks (the likes, dislikes, political standing and areas of interest not only for any given user, but also their social circle), gives insights into how susceptible to advertising a user is, can be used for governmentally sanctioned brainwashing  Time and locations (mostly GPS data, “Who is where, when, how often and with whom?”), very privacy-sensitive data (Xiaomeng, n.d., p. 3) 4.1.1 Business Value Businesses have much interest in big data. It is very valuable for advertisers and, there-fore, the corporations that hire them. In the past, data has been used to review things that already happened. However, the rather descriptive function was unable to provide insights regarding future trends. Big data can – simply because of its incoming speed – enable ana-lysts to draw conclusions from looking at the data movement. Therein lies the value of big data analytics for any corporations: If trends can be predicted, the right reaction to these can be predicted, too. (Xiaomeng, n.d., p. 6) 4.1.2 Big data applications across the industry Prediction and customer segmentation: Many organizations approach big data as a way to easily predict outcomes related to particular consumer groups in a variety of set-tings. Credit scores given by banks and health insurance policies are the most prominent examples. (Xiaomeng, n.d., p. 7) Churn prediction: “In the telecom sector, customer switch from one company to another is called churn.” (Xiaomeng, n.d., p. 7) It is a fact that keeping clients is way less costly than getting new ones. Therefore, corporations are willing to invest money and time into so-called “churn models” (Xiaomeng, n.d., p. 7). The intention here is solely to flag down people at risk of churning to find ways to make them stay. Churn models – where before they had been relying on log data like data usage, and the age of the customer base – now use all accessible data such as web data (if the client has recently checked the cancella-tion policy of the company) and data won from social media to mark customers at risk of churning. (Xiaomeng, n.d., p. 7) Recommendations and personalized advertising: “Recommender system are common in almost every domain. They are used for book recommen-dations on Amazon.com (“customer who bought this item also bought…”), for music recommenda-tions on Spotify, on movie recommendations on Netflix, and on news recommendations on almost all news portal. Some recommendation are based on general trends (e.g. “most read news for today…”), while others are more personalized recommendations (for example, “recommended to you because you have watched…” on Netflix).” (Xiaomeng, n.d., pp. 7″,8) Corporations use algorithms to filter out what kind of advertising people are susceptible to. This allows them to reap the benefits of big data analytics. It enables companies to cater to most of their customers individually. Recommendations make up a big part of most corpo-ration’s sales. (Xiaomeng, n.d., pp. 7″,8) Analysis of sentiments: Data is being used to analyze the trends opinions of a large quantity of people currently take. Analysts can divine from this what the market is thinking, and just what consumer are feeling and wanting from suppliers. This data can mostly be gathered on social media outlets. Through aggressive data mining on social media can get much information about a given person, even though such information is allegedly protect-ed by law. (Xiaomeng, n.d., p. 8) 4.2 PERSONAL DATA – DIFFERING QUALIFIERS 4.3 RELEVANCE OF PERSONAL DATA ON SOCIAL NET-WORKS 4.4 BIG DATA GATHERING AND ITS USES FOR THE ECONO-MY 5 BUSINESS IN BIG DATA 5.1 DATA – THE NEW GOLD STANDARD 5.2 OBSERVATION OF THE MARKET – SIMPLER THAN EVER 5.3 CATEGORIES OF PERSONAL DATA 5.4 BUSINESS MODELS OF FACEBOOK AND SIMILAR COR-PORATIONS 5.5 EXAMPLE: THE CAMBRIDGE ANALYTICA AFFAIR 6 TECHNOLOGICAL ASPECTS 6.1 FINGERPRINTS – WHAT DO WE REALLY LEAVE BEHIND 6.2 PERSONALISED ADVERTISING 6.3 DATA PROTECTION LAWS 7 INFLUENCE OF ONLINE BEHAVIOUR ON POLITICAL SITUATIONS 7.1 POLITICAL OPINIONS – DATA STREAMS 7.2 EFFECTS OF MEDDLING MEDIA 7.3 ALGORITHMS – SURROUNDED BY LIKEMINDED PEOPLE 7.4 EXAMPLE: US PRESIDENTIAL ELECTION 2016 So wie bei jeder technologischen Erfindung und Innovation besteht auch bei dem Sam-meln von Daten – ob nun personenbezogen oder nicht – das Risiko, dass diese in die fal-schen Hände gelangen und missbraucht werden. Ein gutes Beispiel ist die Causa Cambridge Analytica. Die jetzige Entwicklung von „Big Data“ zeigt uns die Gefahren, de-nen unsere Privatssphäre ausgesetzt ist und wie unzureichend Datenschutzgesetze sind, welche vor der Zeit der Tech-Giganten wie Google, Facebook, etc… geschrieben worden sind. (vgl. (Platzhalter1)S2 7.4.1 “Fake News” and the influence thereof 7.4.2 Russian Psy-Ops 7.4.3 Political camps: susceptible to media 7.5 EXAMPLE: RIOTS IN AFRICA 2011 7.5.1 Social media: Technological advances and changes in information spreading 7.5.2 Cultural changes following the rise of social networks 8 CONCLUSION – IS IT POSSIBLE TO INFLUENCE THE ECONOMY AND THE POLITICAL SITUATION THROUGH BIG DATA? 9 LITERATURE AND REFERENCES

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