Today's social bots are sophisticated and sometimes menacing. Indeed, their presence can endanger online ecosystems as well as our society. Social bots populate techno-social systems: they are often benign, or even useful, but some are created to harm, by tampering with, manipulating, and deceiving social media users. Social bots have been used to infiltrate political discourse, manipulate the stock market, steal personal information, and spread misinformation. The detection of social bots is therefore an important research endeavor. A taxonomy of the different social bot detection systems proposed in the literature accounts for network-based techniques, crowdsourcing strategies, feature-based supervised learning, and hybrid systems.
%0 Journal Article
%1 FerraraVarolEtAl16cacm
%A Ferrara, Emilio
%A Varol, Onur
%A Davis, Clayton
%A Menczer, Filippo
%A Flammini, Alessandro
%D 2016
%J Communications of the ACM
%K 01821 acm paper ai secure web social software robot
%N 7
%P 96--104
%R 10.1145/2818717
%T The Rise of Social Bots
%V 59
%X Today's social bots are sophisticated and sometimes menacing. Indeed, their presence can endanger online ecosystems as well as our society. Social bots populate techno-social systems: they are often benign, or even useful, but some are created to harm, by tampering with, manipulating, and deceiving social media users. Social bots have been used to infiltrate political discourse, manipulate the stock market, steal personal information, and spread misinformation. The detection of social bots is therefore an important research endeavor. A taxonomy of the different social bot detection systems proposed in the literature accounts for network-based techniques, crowdsourcing strategies, feature-based supervised learning, and hybrid systems.
@article{FerraraVarolEtAl16cacm,
abstract = {Today's social bots are sophisticated and sometimes menacing. Indeed, their presence can endanger online ecosystems as well as our society. Social bots populate techno-social systems: they are often benign, or even useful, but some are created to harm, by tampering with, manipulating, and deceiving social media users. Social bots have been used to infiltrate political discourse, manipulate the stock market, steal personal information, and spread misinformation. The detection of social bots is therefore an important research endeavor. A taxonomy of the different social bot detection systems proposed in the literature accounts for network-based techniques, crowdsourcing strategies, feature-based supervised learning, and hybrid systems.},
added-at = {2016-11-21T09:25:49.000+0100},
author = {Ferrara, Emilio and Varol, Onur and Davis, Clayton and Menczer, Filippo and Flammini, Alessandro},
biburl = {https://www.bibsonomy.org/bibtex/228a52313ead6c3b9ce73e7d14bf80255/flint63},
doi = {10.1145/2818717},
file = {ACM Digital Library:2016/FerraraVarolEtAl16cacm.pdf:PDF},
groups = {public},
interhash = {86358e924429310663de580b93564a7e},
intrahash = {28a52313ead6c3b9ce73e7d14bf80255},
issn = {0001-0782},
journal = {Communications of the ACM},
keywords = {01821 acm paper ai secure web social software robot},
month = {#jul#},
number = 7,
pages = {96--104},
timestamp = {2018-04-16T12:39:24.000+0200},
title = {The Rise of Social Bots},
username = {flint63},
volume = 59,
year = 2016
}