111 research outputs found
Reverse Engineering Socialbot Infiltration Strategies in Twitter
Data extracted from social networks like Twitter are increasingly being used
to build applications and services that mine and summarize public reactions to
events, such as traffic monitoring platforms, identification of epidemic
outbreaks, and public perception about people and brands. However, such
services are vulnerable to attacks from socialbots automated accounts that
mimic real users seeking to tamper statistics by posting messages generated
automatically and interacting with legitimate users. Potentially, if created in
large scale, socialbots could be used to bias or even invalidate many existing
services, by infiltrating the social networks and acquiring trust of other
users with time. This study aims at understanding infiltration strategies of
socialbots in the Twitter microblogging platform. To this end, we create 120
socialbot accounts with different characteristics and strategies (e.g., gender
specified in the profile, how active they are, the method used to generate
their tweets, and the group of users they interact with), and investigate the
extent to which these bots are able to infiltrate the Twitter social network.
Our results show that even socialbots employing simple automated mechanisms are
able to successfully infiltrate the network. Additionally, using a
factorial design, we quantify infiltration effectiveness of different bot
strategies. Our analysis unveils findings that are key for the design of
detection and counter measurements approaches
Do Social Bots Dream of Electric Sheep? A Categorisation of Social Media Bot Accounts
So-called 'social bots' have garnered a lot of attention lately. Previous
research showed that they attempted to influence political events such as the
Brexit referendum and the US presidential elections. It remains, however,
somewhat unclear what exactly can be understood by the term 'social bot'. This
paper addresses the need to better understand the intentions of bots on social
media and to develop a shared understanding of how 'social' bots differ from
other types of bots. We thus describe a systematic review of publications that
researched bot accounts on social media. Based on the results of this
literature review, we propose a scheme for categorising bot accounts on social
media sites. Our scheme groups bot accounts by two dimensions - Imitation of
human behaviour and Intent.Comment: Accepted for publication in the Proceedings of the Australasian
Conference on Information Systems, 201
Online Human-Bot Interactions: Detection, Estimation, and Characterization
Increasing evidence suggests that a growing amount of social media content is
generated by autonomous entities known as social bots. In this work we present
a framework to detect such entities on Twitter. We leverage more than a
thousand features extracted from public data and meta-data about users:
friends, tweet content and sentiment, network patterns, and activity time
series. We benchmark the classification framework by using a publicly available
dataset of Twitter bots. This training data is enriched by a manually annotated
collection of active Twitter users that include both humans and bots of varying
sophistication. Our models yield high accuracy and agreement with each other
and can detect bots of different nature. Our estimates suggest that between 9%
and 15% of active Twitter accounts are bots. Characterizing ties among
accounts, we observe that simple bots tend to interact with bots that exhibit
more human-like behaviors. Analysis of content flows reveals retweet and
mention strategies adopted by bots to interact with different target groups.
Using clustering analysis, we characterize several subclasses of accounts,
including spammers, self promoters, and accounts that post content from
connected applications.Comment: Accepted paper for ICWSM'17, 10 pages, 8 figures, 1 tabl
- …