3,219 research outputs found
Fame for sale: efficient detection of fake Twitter followers
are those Twitter accounts specifically created to
inflate the number of followers of a target account. Fake followers are
dangerous for the social platform and beyond, since they may alter concepts
like popularity and influence in the Twittersphere - hence impacting on
economy, politics, and society. In this paper, we contribute along different
dimensions. First, we review some of the most relevant existing features and
rules (proposed by Academia and Media) for anomalous Twitter accounts
detection. Second, we create a baseline dataset of verified human and fake
follower accounts. Such baseline dataset is publicly available to the
scientific community. Then, we exploit the baseline dataset to train a set of
machine-learning classifiers built over the reviewed rules and features. Our
results show that most of the rules proposed by Media provide unsatisfactory
performance in revealing fake followers, while features proposed in the past by
Academia for spam detection provide good results. Building on the most
promising features, we revise the classifiers both in terms of reduction of
overfitting and cost for gathering the data needed to compute the features. The
final result is a novel classifier, general enough to thwart
overfitting, lightweight thanks to the usage of the less costly features, and
still able to correctly classify more than 95% of the accounts of the original
training set. We ultimately perform an information fusion-based sensitivity
analysis, to assess the global sensitivity of each of the features employed by
the classifier. The findings reported in this paper, other than being supported
by a thorough experimental methodology and interesting on their own, also pave
the way for further investigation on the novel issue of fake Twitter followers
A Network Topology Approach to Bot Classification
Automated social agents, or bots, are increasingly becoming a problem on
social media platforms. There is a growing body of literature and multiple
tools to aid in the detection of such agents on online social networking
platforms. We propose that the social network topology of a user would be
sufficient to determine whether the user is a automated agent or a human. To
test this, we use a publicly available dataset containing users on Twitter
labelled as either automated social agent or human. Using an unsupervised
machine learning approach, we obtain a detection accuracy rate of 70%
Organized Behavior Classification of Tweet Sets using Supervised Learning Methods
During the 2016 US elections Twitter experienced unprecedented levels of
propaganda and fake news through the collaboration of bots and hired persons,
the ramifications of which are still being debated. This work proposes an
approach to identify the presence of organized behavior in tweets. The Random
Forest, Support Vector Machine, and Logistic Regression algorithms are each
used to train a model with a data set of 850 records consisting of 299 features
extracted from tweets gathered during the 2016 US presidential election. The
features represent user and temporal synchronization characteristics to capture
coordinated behavior. These models are trained to classify tweet sets among the
categories: organic vs organized, political vs non-political, and pro-Trump vs
pro-Hillary vs neither. The random forest algorithm performs better with
greater than 95% average accuracy and f-measure scores for each category. The
most valuable features for classification are identified as user based
features, with media use and marking tweets as favorite to be the most
dominant.Comment: 51 pages, 5 figure
Social Bots: Human-Like by Means of Human Control?
Social bots are currently regarded an influential but also somewhat
mysterious factor in public discourse and opinion making. They are considered
to be capable of massively distributing propaganda in social and online media
and their application is even suspected to be partly responsible for recent
election results. Astonishingly, the term `Social Bot' is not well defined and
different scientific disciplines use divergent definitions. This work starts
with a balanced definition attempt, before providing an overview of how social
bots actually work (taking the example of Twitter) and what their current
technical limitations are. Despite recent research progress in Deep Learning
and Big Data, there are many activities bots cannot handle well. We then
discuss how bot capabilities can be extended and controlled by integrating
humans into the process and reason that this is currently the most promising
way to go in order to realize effective interactions with other humans.Comment: 36 pages, 13 figure
Detecting Real-World Influence Through Twitter
In this paper, we investigate the issue of detecting the real-life influence
of people based on their Twitter account. We propose an overview of common
Twitter features used to characterize such accounts and their activity, and
show that these are inefficient in this context. In particular, retweets and
followers numbers, and Klout score are not relevant to our analysis. We thus
propose several Machine Learning approaches based on Natural Language
Processing and Social Network Analysis to label Twitter users as Influencers or
not. We also rank them according to a predicted influence level. Our proposals
are evaluated over the CLEF RepLab 2014 dataset, and outmatch state-of-the-art
ranking methods.Comment: 2nd European Network Intelligence Conference (ENIC), Sep 2015,
Karlskrona, Swede
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
Influence of augmented humans in online interactions during voting events
The advent of the digital era provided a fertile ground for the development
of virtual societies, complex systems influencing real-world dynamics.
Understanding online human behavior and its relevance beyond the digital
boundaries is still an open challenge. Here we show that online social
interactions during a massive voting event can be used to build an accurate map
of real-world political parties and electoral ranks. We provide evidence that
information flow and collective attention are often driven by a special class
of highly influential users, that we name "augmented humans", who exploit
thousands of automated agents, also known as bots, for enhancing their online
influence. We show that augmented humans generate deep information cascades, to
the same extent of news media and other broadcasters, while they uniformly
infiltrate across the full range of identified groups. Digital augmentation
represents the cyber-physical counterpart of the human desire to acquire power
within social systems.Comment: 11 page
Social Fingerprinting: detection of spambot groups through DNA-inspired behavioral modeling
Spambot detection in online social networks is a long-lasting challenge
involving the study and design of detection techniques capable of efficiently
identifying ever-evolving spammers. Recently, a new wave of social spambots has
emerged, with advanced human-like characteristics that allow them to go
undetected even by current state-of-the-art algorithms. In this paper, we show
that efficient spambots detection can be achieved via an in-depth analysis of
their collective behaviors exploiting the digital DNA technique for modeling
the behaviors of social network users. Inspired by its biological counterpart,
in the digital DNA representation the behavioral lifetime of a digital account
is encoded in a sequence of characters. Then, we define a similarity measure
for such digital DNA sequences. We build upon digital DNA and the similarity
between groups of users to characterize both genuine accounts and spambots.
Leveraging such characterization, we design the Social Fingerprinting
technique, which is able to discriminate among spambots and genuine accounts in
both a supervised and an unsupervised fashion. We finally evaluate the
effectiveness of Social Fingerprinting and we compare it with three
state-of-the-art detection algorithms. Among the peculiarities of our approach
is the possibility to apply off-the-shelf DNA analysis techniques to study
online users behaviors and to efficiently rely on a limited number of
lightweight account characteristics
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