77,232 research outputs found
Imminent Domain Name: The Technological Land-Grab and ICANN\u27s Lifting of Domain Name Restrictions
Making the Most of Tweet-Inherent Features for Social Spam Detection on Twitter
Social spam produces a great amount of noise on social media services such as
Twitter, which reduces the signal-to-noise ratio that both end users and data
mining applications observe. Existing techniques on social spam detection have
focused primarily on the identification of spam accounts by using extensive
historical and network-based data. In this paper we focus on the detection of
spam tweets, which optimises the amount of data that needs to be gathered by
relying only on tweet-inherent features. This enables the application of the
spam detection system to a large set of tweets in a timely fashion, potentially
applicable in a real-time or near real-time setting. Using two large
hand-labelled datasets of tweets containing spam, we study the suitability of
five classification algorithms and four different feature sets to the social
spam detection task. Our results show that, by using the limited set of
features readily available in a tweet, we can achieve encouraging results which
are competitive when compared against existing spammer detection systems that
make use of additional, costly user features. Our study is the first that
attempts at generalising conclusions on the optimal classifiers and sets of
features for social spam detection over different datasets
Citation gaming induced by bibliometric evaluation: a country-level comparative analysis
It is several years since national research evaluation systems around the
globe started making use of quantitative indicators to measure the performance
of researchers. Nevertheless, the effects on these systems on the behavior of
the evaluated researchers are still largely unknown. We attempt to shed light
on this topic by investigating how Italian researchers reacted to the
introduction in 2011 of national regulations in which key passages of
professional careers are governed by bibliometric indicators. A new inwardness
measure, able to gauge the degree of scientific self-referentiality of a
country, is defined as the proportion of citations coming from the country
itself compared to the total number of citations gathered by the country.
Compared to the trends of the other G10 countries in the period 2000-2016,
Italy's inwardness shows a net increase after the introduction of the new
evaluation rules. Indeed, globally and also for a large majority of the
research fields, Italy became the European country with the highest inwardness.
Possible explanations are proposed and discussed, concluding that the observed
trends are strongly suggestive of a generalized strategic use of citations,
both in the form of author self-citations and of citation clubs. We argue that
the Italian case offers crucial insights on the constitutive effects of
evaluation systems. As such, it could become a paradigmatic case in the debate
about the use of indicators in science-policy contexts
Better Safe Than Sorry: An Adversarial Approach to Improve Social Bot Detection
The arm race between spambots and spambot-detectors is made of several cycles
(or generations): a new wave of spambots is created (and new spam is spread),
new spambot filters are derived and old spambots mutate (or evolve) to new
species. Recently, with the diffusion of the adversarial learning approach, a
new practice is emerging: to manipulate on purpose target samples in order to
make stronger detection models. Here, we manipulate generations of Twitter
social bots, to obtain - and study - their possible future evolutions, with the
aim of eventually deriving more effective detection techniques. In detail, we
propose and experiment with a novel genetic algorithm for the synthesis of
online accounts. The algorithm allows to create synthetic evolved versions of
current state-of-the-art social bots. Results demonstrate that synthetic bots
really escape current detection techniques. However, they give all the needed
elements to improve such techniques, making possible a proactive approach for
the design of social bot detection systems.Comment: This is the pre-final version of a paper accepted @ 11th ACM
Conference on Web Science, June 30-July 3, 2019, Boston, U
How journal rankings can suppress interdisciplinary research. A comparison between Innovation Studies and Business & Management
This study provides quantitative evidence on how the use of journal rankings
can disadvantage interdisciplinary research in research evaluations. Using
publication and citation data, it compares the degree of interdisciplinarity
and the research performance of a number of Innovation Studies units with that
of leading Business & Management schools in the UK. On the basis of various
mappings and metrics, this study shows that: (i) Innovation Studies units are
consistently more interdisciplinary in their research than Business &
Management schools; (ii) the top journals in the Association of Business
Schools' rankings span a less diverse set of disciplines than lower-ranked
journals; (iii) this results in a more favourable assessment of the performance
of Business & Management schools, which are more disciplinary-focused. This
citation-based analysis challenges the journal ranking-based assessment. In
short, the investigation illustrates how ostensibly 'excellence-based' journal
rankings exhibit a systematic bias in favour of mono-disciplinary research. The
paper concludes with a discussion of implications of these phenomena, in
particular how the bias is likely to affect negatively the evaluation and
associated financial resourcing of interdisciplinary research organisations,
and may result in researchers becoming more compliant with disciplinary
authority over time.Comment: 41 pages, 10 figure
Identifying Native Applications with High Assurance
The work described in this paper investigates the problem
of identifying and deterring stealthy malicious processes on
a host. We point out the lack of strong application iden-
tication in main stream operating systems. We solve the
application identication problem by proposing a novel iden-
tication model in which user-level applications are required
to present identication proofs at run time to be authenti-
cated by the kernel using an embedded secret key. The se-
cret key of an application is registered with a trusted kernel
using a key registrar and is used to uniquely authenticate
and authorize the application. We present a protocol for
secure authentication of applications. Additionally, we de-
velop a system call monitoring architecture that uses our
model to verify the identity of applications when making
critical system calls. Our system call monitoring can be
integrated with existing policy specication frameworks to
enforce application-level access rights. We implement and
evaluate a prototype of our monitoring architecture in Linux
as device drivers with nearly no modication of the ker-
nel. The results from our extensive performance evaluation
shows that our prototype incurs low overhead, indicating the
feasibility of our model
Citation Counts and Evaluation of Researchers in the Internet Age
Bibliometric measures derived from citation counts are increasingly being
used as a research evaluation tool. Their strengths and weaknesses have been
widely analyzed in the literature and are often subject of vigorous debate. We
believe there are a few fundamental issues related to the impact of the web
that are not taken into account with the importance they deserve. We focus on
evaluation of researchers, but several of our arguments may be applied also to
evaluation of research institutions as well as of journals and conferences.Comment: 4 pages, 2 figures, 3 table
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