10,671 research outputs found
Fake View Analytics in Online Video Services
Online video-on-demand(VoD) services invariably maintain a view count for
each video they serve, and it has become an important currency for various
stakeholders, from viewers, to content owners, advertizers, and the online
service providers themselves. There is often significant financial incentive to
use a robot (or a botnet) to artificially create fake views. How can we detect
the fake views? Can we detect them (and stop them) using online algorithms as
they occur? What is the extent of fake views with current VoD service
providers? These are the questions we study in the paper. We develop some
algorithms and show that they are quite effective for this problem.Comment: 25 pages, 15 figure
Understanding the Detection of View Fraud in Video Content Portals
While substantial effort has been devoted to understand fraudulent activity
in traditional online advertising (search and banner), more recent forms such
as video ads have received little attention. The understanding and
identification of fraudulent activity (i.e., fake views) in video ads for
advertisers, is complicated as they rely exclusively on the detection
mechanisms deployed by video hosting portals. In this context, the development
of independent tools able to monitor and audit the fidelity of these systems
are missing today and needed by both industry and regulators.
In this paper we present a first set of tools to serve this purpose. Using
our tools, we evaluate the performance of the audit systems of five major
online video portals. Our results reveal that YouTube's detection system
significantly outperforms all the others. Despite this, a systematic evaluation
indicates that it may still be susceptible to simple attacks. Furthermore, we
find that YouTube penalizes its videos' public and monetized view counters
differently, the former being more aggressive. This means that views identified
as fake and discounted from the public view counter are still monetized. We
speculate that even though YouTube's policy puts in lots of effort to
compensate users after an attack is discovered, this practice places the burden
of the risk on the advertisers, who pay to get their ads displayed.Comment: To appear in WWW 2016, Montr\'eal, Qu\'ebec, Canada. Please cite the
conference version of this pape
Dancing to the Partisan Beat: A First Analysis of Political Communication on TikTok
TikTok is a video-sharing social networking service, whose popularity is
increasing rapidly. It was the world's second-most downloaded app in 2019.
Although the platform is known for having users posting videos of themselves
dancing, lip-syncing, or showcasing other talents, user-videos expressing
political views have seen a recent spurt. This study aims to perform a primary
evaluation of political communication on TikTok. We collect a set of US
partisan Republican and Democratic videos to investigate how users communicated
with each other about political issues. With the help of computer vision,
natural language processing, and statistical tools, we illustrate that
political communication on TikTok is much more interactive in comparison to
other social media platforms, with users combining multiple information
channels to spread their messages. We show that political communication takes
place in the form of communication trees since users generate branches of
responses to existing content. In terms of user demographics, we find that
users belonging to both the US parties are young and behave similarly on the
platform. However, Republican users generated more political content and their
videos received more responses; on the other hand, Democratic users engaged
significantly more in cross-partisan discussions.Comment: Accepted as a full paper at the 12th International ACM Web Science
Conference (WebSci 2020). Please cite the WebSci version; Second version
includes corrected typo
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