1,047 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
Dynamics of private social networks
Social networks, have been a significant turning point in ways individuals and companies interact. Various research has also revolved around public social networks, such as Twitter and Facebook. In most cases trying to understand what's happening in the network such predicting trends, and identifying natural phenomenon. Seeing the growth of public social networks several corporations have sought to build their own private networks to enable their staff to share knowledge, and expertise. Little research has been done in regards to the value private networks give to their stake holders. This is primarily due to the fact as their name implies, these networks are private, thus access to internal data is limited to a trusted few. This paper looks at a particular online private social network, and seeks to investigate the research possibilities made available, and how this can bring value to the organisation which runs the network. Notwithstanding the limitations of the network, this paper seeks to explore the connections graph between members of the network, as well as understanding the topics discussed within the network. The findings show that by visualising a social network one can assess the success or failure of their online networks. The Analysis conducted can also identify skill shortages within areas of the network, thus allowing corporations to take action and rectify any potential problems.peer-reviewe
NOFACE: A new framework for irrelevant content filtering in social media according to credibility and expertise
Social networks have taken an irreplaceable role in our lives. They are used daily by millions of people
to communicate and inform themselves. This success has also led to a lot of irrelevant content and even
misinformation on social media. In this paper, we propose a user-centred framework to reduce the amount
of irrelevant content in social networks to support further stages of data mining processes. The system also
helps in the reduction of misinformation in social networks, since it selects credible and reputable users. The
system is based on the belief that if a user is credible then their content will be credible. Our proposal uses
word embeddings in a first stage, to create a set of interesting users according to their expertise. After that, in
a later stage, it employs social network metrics to further narrow down the relevant users according to their
credibility in the network. To validate the framework, it has been tested with two real Big Data problems on
Twitter. One related to COVID-19 tweets and the other to last United States elections on 3rd November. Both
are problems in which finding relevant content may be difficult due to the large amount of data published
during the last years. The proposed framework, called NOFACE, reduces the number of irrelevant users posting
about the topic, taking only those that have a higher credibility, and thus giving interesting information
about the selected topic. This entails a reduction of irrelevant information, mitigating therefore the presence
of misinformation on a posterior data mining method application, improving the obtained results, as it is
illustrated in the mentioned two topics using clustering, association rules and LDA techniques.European Commission 786687Andalusian government
FEDER operative program P18-RT-2947
B-TIC-145-UGR18University of Granada's internal plan PPJIB2021-04Spanish Government FPU18/0015
Partisan Asymmetries in Online Political Activity
We examine partisan differences in the behavior, communication patterns and
social interactions of more than 18,000 politically-active Twitter users to
produce evidence that points to changing levels of partisan engagement with the
American online political landscape. Analysis of a network defined by the
communication activity of these users in proximity to the 2010 midterm
congressional elections reveals a highly segregated, well clustered partisan
community structure. Using cluster membership as a high-fidelity (87% accuracy)
proxy for political affiliation, we characterize a wide range of differences in
the behavior, communication and social connectivity of left- and right-leaning
Twitter users. We find that in contrast to the online political dynamics of the
2008 campaign, right-leaning Twitter users exhibit greater levels of political
activity, a more tightly interconnected social structure, and a communication
network topology that facilitates the rapid and broad dissemination of
political information.Comment: 17 pages, 10 figures, 6 table
Data Portraits and Intermediary Topics: Encouraging Exploration of Politically Diverse Profiles
In micro-blogging platforms, people connect and interact with others.
However, due to cognitive biases, they tend to interact with like-minded people
and read agreeable information only. Many efforts to make people connect with
those who think differently have not worked well. In this paper, we
hypothesize, first, that previous approaches have not worked because they have
been direct -- they have tried to explicitly connect people with those having
opposing views on sensitive issues. Second, that neither recommendation or
presentation of information by themselves are enough to encourage behavioral
change. We propose a platform that mixes a recommender algorithm and a
visualization-based user interface to explore recommendations. It recommends
politically diverse profiles in terms of distance of latent topics, and
displays those recommendations in a visual representation of each user's
personal content. We performed an "in the wild" evaluation of this platform,
and found that people explored more recommendations when using a biased
algorithm instead of ours. In line with our hypothesis, we also found that the
mixture of our recommender algorithm and our user interface, allowed
politically interested users to exhibit an unbiased exploration of the
recommended profiles. Finally, our results contribute insights in two aspects:
first, which individual differences are important when designing platforms
aimed at behavioral change; and second, which algorithms and user interfaces
should be mixed to help users avoid cognitive mechanisms that lead to biased
behavior.Comment: 12 pages, 7 figures. To be presented at ACM Intelligent User
Interfaces 201
Interest communities and flow roles in directed networks: the Twitter network of the UK riots
Directionality is a crucial ingredient in many complex networks in which
information, energy or influence are transmitted. In such directed networks,
analysing flows (and not only the strength of connections) is crucial to reveal
important features of the network that might go undetected if the orientation
of connections is ignored. We showcase here a flow-based approach for community
detection in networks through the study of the network of the most influential
Twitter users during the 2011 riots in England. Firstly, we use directed Markov
Stability to extract descriptions of the network at different levels of
coarseness in terms of interest communities, i.e., groups of nodes within which
flows of information are contained and reinforced. Such interest communities
reveal user groupings according to location, profession, employer, and topic.
The study of flows also allows us to generate an interest distance, which
affords a personalised view of the attention in the network as viewed from the
vantage point of any given user. Secondly, we analyse the profiles of incoming
and outgoing long-range flows with a combined approach of role-based similarity
and the novel relaxed minimum spanning tree algorithm to reveal that the users
in the network can be classified into five roles. These flow roles go beyond
the standard leader/follower dichotomy and differ from classifications based on
regular/structural equivalence. We then show that the interest communities fall
into distinct informational organigrams characterised by a different mix of
user roles reflecting the quality of dialogue within them. Our generic
framework can be used to provide insight into how flows are generated,
distributed, preserved and consumed in directed networks.Comment: 32 pages, 14 figures. Supplementary Spreadsheet available from:
http://www2.imperial.ac.uk/~mbegueri/Docs/riotsCommunities.zip or
http://rsif.royalsocietypublishing.org/content/11/101/20140940/suppl/DC
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