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How Social Media Analytics is aiding the Political communication: An interesting relationship between politics and analytics I always get amazed when I think about the world before and after social media. I can’t think of a single thing which is not impacted by social media and “Politics” is one of them. Nowadays, almost every well-known politician or a political party has an active twitter or a Facebook page with an objective to connect with people in a better way. In this blog, I will talk about the importance of social media analytics in politics, current trends and finally wrap up with popular tools used for the sentiment/content analysis and network analysis of data extracted from social media. According to a study led by the University of California, San Diego, about one third of a million more people voted in the United States in 2010 because of a single Facebook message on Election Day. This shows the power of social media on people. This trend is catching up in emerging countries like India as well. Therefore, there is an emerging need to continuously collect, monitor, analyze, summarize, and visualize politically relevant information from social media. These activities are considered difficult tasks due to a large numbers of different social media platforms as well as the large amount and complexity of information and data. And this is where social media analytics comes to the rescue. The very first step regarding data collection is to determine the sources of the data. With skyrocketing growth in the user base in recent years, social network sites and micro blogging services with their most prominent examples Facebook and Twitter offer a huge amount of politically relevant user-generated content in an unprecedented scope. Both Twitter and Facebook offer application programing interfaces (API) for data tracking. The most frequently used API for Twitter are ‘‘Search API’’ and ‘‘Streaming API’’, while Facebook’s ‘‘Graph API’’ allows programmers to conveniently track Wall postings (i.e., status updates and comments). We can also implement the functionality of network visualization by means of the API provided by the open-source social network analysis software ‘‘Gephi’’. 1) 2) 3) 4) 5) The different approaches of data tracking for a political institution which depend on the specific intentions of that institution can be explained as follows: Self-involved: It is applicable when an individual politician or a political party want to know explicitly how people are talking about them in social media. It includes collection of all tweets, hashtags, posts, comments/likes etc on social media platform. Keyword/topic-based: Here tweets as well as Facebook and blog postings that involve keywords related to topics of interest can be tracked. To attain a high level of data completeness, relevant keywords representing the topic of interest have to be carefully and systematically chosen in advance. The broader the topic to be analyzed, the more keywords should be taken into account. Actor based: There are usually a number of actors who can be considered as more influential or more popular than most other users. These actors are said to have the power to influence opinion making processes. Therefore, politicians or political parties are also interested in monitoring such important users in terms of their generated content. For that, an actor-based tracking approach might be employed to track tweets, wall posts, etc. Random/exploratory: Here, tracking approach is to randomly select one or several sets of data (tweets, Facebook or blog postings) for different time periods for analysis. Based on these random Social media and political communication datasets, particularly content analysis might be conducted to identify major political topics and detect users’ opinions or sentiment associated with those topics. URL based: Given that social media platforms are widely used, among other purposes, to disseminate information, particularly by means of posting URL, political actors might also apply a URL-based approach to selectively track contents behind hyperlinks shared in tweets, Facebook and blog postings. This might provide additional meaningful insights, especially in case of tweets with a limited length of 140 characters. Social Media Analytics Framework in political context Popular tools used for social media analytics in politically relevant user generated content: a) Automated content analysis/text mining: WordStat, LIWC, General Inquirer, etc b) Manual content analysis/text mining: ATLAS.ti, QDAMiner, The Ethnograph, etc. c) Opinion mining/sentiment analysis: SentiStrength, PolArt, SentiWordNet, etc. d) Social network analysis: Gephi, UCINET, Pajek, etc.