You’re probably not aware of the fact that the most of the modern political campaigns use the power of Data Science and Social Networking to analyse your profile and to tell you the message you actually want to hear.
For example, you see the advertised political message on Facebook you really agree with, and you think this is a right political option for you, because they tackle the problem you consider as important and in the way you think it’s right. However, they probably don’t. They just use social networking data to identify your preferences, beliefs, wishes and feelings to deliver to you the most appropriate message you want to hear. Based on the profile information you voluntarily filled in, your likes, shares, views and keywords from your comments, it is not a problem for various Data Science algorithms to identify your feelings, beliefs, preferences and wishes. Consequently, according to that information the most appropriate and almost individually-tailored political advertising message is delivered to you to win your sympathy and the potential vote.
Additionally, the information about the most liked content from the social networking can be used to identify most appropriate content for the whole political campaign. I explained this process in one of my previous article that describes the strategy to win political elections called DAVA.
Outside Social Networking, the similar data-research-based tactics are used to identify appropriate content for the political campaigns. For example, using various statistical information the majority and the most common attribute of the majority of the voters is identified. That attribute is used as a basis for political campaign content and for the definition of the aimed group. Further on, oppressing silent topics that have huge emotional impact on the members of the aimed group are identified through various focus groups, public opinion research, or even using possibilities offered by the internet. This is actually identification of the biggest problems that the members of the aimed group see as important. Those are then used to improve the base content for the political campaign. Such content is than cross-referenced with the topics on which the members of same group actively react and that causes their attention. The final stage would be to ask the aimed group about the most appropriate solutions for the problems they identified as important, and to incorporate their ideas into political campaign.
At the end of the process, after analysing data you provided with various Data Science methods, political entities have campaign content that is relevant for the majority of the potential voters, that has emotional impact on them, on which they actively react, that causes their attention, and that proposes the solutions that the majority itself sees as appropriate.
However, it is not always about what you want or expect to hear. Some political entities go even further and want to change your opinion or attitude towards important issues. This is not a problem if they lead transparent discussion that fits to the legal and moral framework of the society. However, sometimes they try to achieve this from the grey zone of the political campaigning. Meaning, they use unethical methods to achieve their aims and to change the opinion of the potential voters to comply with their political strategy; thus, resulting in voting for them at the end. With the rise of the internet-based social networking, this became quite easy to do.
Trolling is the probably most known unethical concept used today. It is the internet-based social network concept, where numerous fake accounts are used to create the illusion about the opinion of the majority regarding specific issue. Trolling as well uses Data and various Data Science methods to identify appropriate content and behaviour. Most of the people are actually ready to change their opinion in regard to specific issue if they feel that the majority has “that” different opinion too. For example, several thousand of automated fake accounts are used to positively comment or appraise Facebook or Twitter post of the specific politician, thus creating the illusion to the rest of the audience that such opinion has approval of the wider public. In that case, an average, real user could change his opinion as well, just on the basis that he has a feeling that majority thinks differently; thus, taking it as a “proper” way of thinking. A normal user, that is not willing to change his opinion just on the basis that the majority thinks differently, would probably give up defending his opinion and the point of view, because he would feel insignificant in the overhleming bunch of people thinking differently.
At the beginning of the trolls’ era, it was quite easy to identify the posts of the potential trolls. However, as Data Analysis and Artificial Intelligence methods extremely improved in the last decade, the trolling itself improved significantly as well. The content of the new, improved trolls is very hard to distinct from the content of the real persons – if not impossible.