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ShareScore release 0.9.0
Dataset results
3 results for “German federal election 2017”
btw17 query auto completion - query suggestions for German politicians and parties before the federal election 2017
<p>The dataset contains the query suggestions for 5 major German parties (terms: "afd", "csu", "dielinke", "fdp", "grüne", "spd") and ten popular politicians and party leaders (terms: "Alexander Gauland", "Alice Weidel", "Angela Merkel", "Cem Özdemir", "Christian Lindner", "Dietmar Bartsch", "Katrin Göring-Eckardt", "Martin Schulz", "Sahra Wagenknecht").</p> <p>The data was crawled on (mostly) two times per day from Tue Aug 04, 2017 to Tue Oct 31, 2017. The dataset contains 20001 suggestions from Bing search (http://api.bing.net/osjson.aspx), 11935 suggestions from Duck-Duck-Go (https://duckduckgo.com/ac/) and 33521 suggestions from Google search (http://clients1.google.de/complete/search). Note, that for some terms and dates no suggestions were returned by some of the APIs.</p> <p>German language settings were used for Google and Bing, English language setting was used for Duck-Duck-Go. The API requests were sent with an IP address from Cologne, Germany. </p> <p>The UTF-8 encoded comma separated text file contains the following columns:</p> <p><source>: google, bing or ddg</p> <p><queryterm>: the query term</p> <p><date>: the date and time of the API call formatted as ISO8601</p> <p><suggestterm>: the suggested query completion (the query term was removed from the suggestion)</p> <p><position>: the position of the query suggestion within the list returned by the API (ranges from 0 to 19)</p> <p> </p> <p> </p> <p><br> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p>
The #BTW17 Twitter Dataset - Recorded Tweets of the Federal Election Campaigns of 2017 for the 19th German Bundestag
<p>The German Bundestag elections are the most important democratic elections of Germany. This dataset comprises Twitter interactions related with German politicians of the most important political parties over several months in the (pre-)phase of the German election campaigns in 2017. The Twitter accounts of 364 politicians (that is approximately half of the German parliament, the German Bundestag) were followed for almost half a year. The collected data comprise of about 10 GB of Twitter raw data generated by more than 120.000 active Twitter users generating more than 1.200.000 tweets during the pre- and hot-phase of the election campaigns for the 19th German Bundestag. <br> The dataset can be used to study how political parties, their followers and supporters make use of social media channels like Twitter in the context of political election campaigns and what kind of content is shared.</p> <p>The following files contain relevant context information:</p> <ul> <li><strong>crawled-pages.json</strong> contains the URLs of the official party faction websites of the 18th German Bundestag that were crawled to identify the Twitter screennames of German politicians of all Bundestag factions. Because the <em>Alternative für Deutschland (AfD)</em> and the <em>Freie Demokratische Partei (FDP)</em> were not part of the 18th German Bundestag (but it was likely that they will enter the 19th German Bundestag) other official websites were selected to crawl for relevant and representative politicians for these both parties (in case of the <em>AfD</em> this was the website of the directorate of the <em>AfD</em> federal party and the list of members of the European Parliament, in case of the <em>FDP</em> this was the website of the executive committee of the <em>FDP</em> federal party of Germany).</li> <li><strong>followed-accounts.json</strong> contains the (manually checked and edited) crawling result of 327 Twitter screennames of politicians that have been observed via the Twitter streaming API to collect this dataset.</li> </ul>
RWI-GEO-VOTE: Vote Share of the German Federal Election 2017 on grid level
<p>The dataset contains the election results of the 2017 Bundestag election on a 1 x 1 km grid level. For this purpose, shapefiles of the electoral districts were distributed across the grids according to availability or grids were assigned to the nearest polling station. If neither a shapefile nor the address of the polling station was available, the votes were distributed to the grids on a population-proportional basis at the municipality level.</p>
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