In the internet era, the information that can be found about politicians online can influence events such as the results of elections. Research has shown that biased search rankings can shift the voting preferences of undecided voters. This shows the importance of studying online search behaviour, especially in the pre-elections phase, when search results can have a particular influence on the future political scene of a country. This master thesis aimed to study the behaviour of online search engines in a period before the German federal election in 2017. The aim was to ascertain if there is any pattern to be found in the auto-suggestions for searches related to politicians. In order to gather data for this experiment, a crawler browsed search engine web pages, input a name and a surname of a politician, and saved that together with all autosuggestions from the search engine. The autosuggestions were prepared for the analysis and divided into semantic groups with the help of clustering algorithms. Different statistical methods, such as correlation analysis, regression analysis, and clustering were used to identify patterns in the data. The research showed that there are no particularly strong patterns in the autosuggestions for searches related to politician’s names. Only moderate dependence was found between gender and personal topics, and showed that a higher amount of personal information autosuggestions correspond more to female politicians.