Social media usage has witnessed a big surge in recent years. Having started as a socialization platform at a very small scale, it has now helped itself into every domain possible. Of them include, advertising products, sharing news, driving businesses and much more. In this work, the influence of social media on the German 2017 Bundestag elections is examined and analyses were performed to show successful predictions. The 10 GB twitter dataset consists of more than 1,200,000 tweets which cover more than 120,000 users. The data set included members of the party and those users who retweeted their tweets inclusive of the tweets with “#BTW17” hashtag. Moreover, twitter API was used to separately get the list of followers of each member of the party. Using the above data set, graph modelling is done using Neo4j. User-user follow relationship and User-user retweet relationships are modelled for better analysis of the data. Various centrality measures were calculated to determine influential persons that drove the election campaign. The results are then compared with the ground-truth data to better understand whether social media has any significant effect on election results. © BEIESP.