%0 Journal Article %1 bessi2016social %A Bessi, Alessandro %A Ferrara, Emilio %D 2016 %J First Monday %K oeffentlichkeit %T Social bots distort the 2016 U.S. Presidential election online discussion %U http://firstmonday.org/ojs/index.php/fm/article/view/7090 %X Social media have been extensively praised for increasing democratic discussion on social issues related to policy and politics. However, what happens when this powerful communication tools are exploited to manipulate online discussion, to change the public perception of political entities, or even to try affecting the outcome of political elections? In this study we investigated how the presence of social media bots, algorithmically driven entities that on the surface appear as legitimate users, affect political discussion around the 2016 U.S. Presidential election. By leveraging state-of-the-art social bot detection algorithms, we uncovered a large fraction of user population that may not be human, accounting for a significant portion of generated content (about one-fifth of the entire conversation). We inferred political partisanships from hashtag adoption, for both humans and bots, and studied spatio-temporal communication, political support dynamics, and influence mechanisms by discovering the level of network embeddedness of the bots. Our findings suggest that the presence of social media bots can indeed negatively affect democratic political discussion rather than improving it, which in turn can potentially alter public opinion and endanger the integrity of the Presidential election.

@article{bessi2016social, abstract = { Social media have been extensively praised for increasing democratic discussion on social issues related to policy and politics. However, what happens when this powerful communication tools are exploited to manipulate online discussion, to change the public perception of political entities, or even to try affecting the outcome of political elections? In this study we investigated how the presence of social media bots, algorithmically driven entities that on the surface appear as legitimate users, affect political discussion around the 2016 U.S. Presidential election. By leveraging state-of-the-art social bot detection algorithms, we uncovered a large fraction of user population that may not be human, accounting for a significant portion of generated content (about one-fifth of the entire conversation). We inferred political partisanships from hashtag adoption, for both humans and bots, and studied spatio-temporal communication, political support dynamics, and influence mechanisms by discovering the level of network embeddedness of the bots. Our findings suggest that the presence of social media bots can indeed negatively affect democratic political discussion rather than improving it, which in turn can potentially alter public opinion and endanger the integrity of the Presidential election. }, added-at = {2016-11-07T00:18:56.000+0100}, author = {Bessi, Alessandro and Ferrara, Emilio}, biburl = {https://www.bibsonomy.org/bibtex/2d0c9ce1570b69f6a54f36dd09a7fca1e/schneeschmelze}, description = {Social bots distort the 2016 U.S. Presidential election online discussion | Bessi | First Monday}, id = {7090, 10.5210/fm.v21i11.7090, http://firstmonday.org/ojs/index.php/fm/article/view/7090}, interhash = {43387b5e26b0af36fdfbbb1aa33e509e}, intrahash = {d0c9ce1570b69f6a54f36dd09a7fca1e}, journal = {First Monday}, keywords = {oeffentlichkeit}, month = {November}, timestamp = {2016-12-04T01:22:33.000+0100}, title = {Social bots distort the 2016 U.S. Presidential election online discussion}, type = {Text.Serial.Journal}, url = {http://firstmonday.org/ojs/index.php/fm/article/view/7090}, year = 2016 }