15,598 research outputs found
A Quantitative Approach to Understanding Online Antisemitism
A new wave of growing antisemitism, driven by fringe Web communities, is an
increasingly worrying presence in the socio-political realm. The ubiquitous and
global nature of the Web has provided tools used by these groups to spread
their ideology to the rest of the Internet. Although the study of antisemitism
and hate is not new, the scale and rate of change of online data has impacted
the efficacy of traditional approaches to measure and understand these
troubling trends. In this paper, we present a large-scale, quantitative study
of online antisemitism. We collect hundreds of million posts and images from
alt-right Web communities like 4chan's Politically Incorrect board (/pol/) and
Gab. Using scientifically grounded methods, we quantify the escalation and
spread of antisemitic memes and rhetoric across the Web. We find the frequency
of antisemitic content greatly increases (in some cases more than doubling)
after major political events such as the 2016 US Presidential Election and the
"Unite the Right" rally in Charlottesville. We extract semantic embeddings from
our corpus of posts and demonstrate how automated techniques can discover and
categorize the use of antisemitic terminology. We additionally examine the
prevalence and spread of the antisemitic "Happy Merchant" meme, and in
particular how these fringe communities influence its propagation to more
mainstream communities like Twitter and Reddit. Taken together, our results
provide a data-driven, quantitative framework for understanding online
antisemitism. Our methods serve as a framework to augment current qualitative
efforts by anti-hate groups, providing new insights into the growth and spread
of hate online.Comment: To appear at the 14th International AAAI Conference on Web and Social
Media (ICWSM 2020). Please cite accordingl
Detecting and Tracking the Spread of Astroturf Memes in Microblog Streams
Online social media are complementing and in some cases replacing
person-to-person social interaction and redefining the diffusion of
information. In particular, microblogs have become crucial grounds on which
public relations, marketing, and political battles are fought. We introduce an
extensible framework that will enable the real-time analysis of meme diffusion
in social media by mining, visualizing, mapping, classifying, and modeling
massive streams of public microblogging events. We describe a Web service that
leverages this framework to track political memes in Twitter and help detect
astroturfing, smear campaigns, and other misinformation in the context of U.S.
political elections. We present some cases of abusive behaviors uncovered by
our service. Finally, we discuss promising preliminary results on the detection
of suspicious memes via supervised learning based on features extracted from
the topology of the diffusion networks, sentiment analysis, and crowdsourced
annotations
Context Aware Computing for The Internet of Things: A Survey
As we are moving towards the Internet of Things (IoT), the number of sensors
deployed around the world is growing at a rapid pace. Market research has shown
a significant growth of sensor deployments over the past decade and has
predicted a significant increment of the growth rate in the future. These
sensors continuously generate enormous amounts of data. However, in order to
add value to raw sensor data we need to understand it. Collection, modelling,
reasoning, and distribution of context in relation to sensor data plays
critical role in this challenge. Context-aware computing has proven to be
successful in understanding sensor data. In this paper, we survey context
awareness from an IoT perspective. We present the necessary background by
introducing the IoT paradigm and context-aware fundamentals at the beginning.
Then we provide an in-depth analysis of context life cycle. We evaluate a
subset of projects (50) which represent the majority of research and commercial
solutions proposed in the field of context-aware computing conducted over the
last decade (2001-2011) based on our own taxonomy. Finally, based on our
evaluation, we highlight the lessons to be learnt from the past and some
possible directions for future research. The survey addresses a broad range of
techniques, methods, models, functionalities, systems, applications, and
middleware solutions related to context awareness and IoT. Our goal is not only
to analyse, compare and consolidate past research work but also to appreciate
their findings and discuss their applicability towards the IoT.Comment: IEEE Communications Surveys & Tutorials Journal, 201
A rule dynamics approach to event detection in Twitter with its application to sports and politics
The increasing popularity of Twitter as social network tool for opinion expression as well as informa- tion retrieval has resulted in the need to derive computational means to detect and track relevant top- ics/events in the network. The application of topic detection and tracking methods to tweets enable users to extract newsworthy content from the vast and somehow chaotic Twitter stream. In this paper, we ap- ply our technique named Transaction-based Rule Change Mining to extract newsworthy hashtag keywords present in tweets from two different domains namely; sports (The English FA Cup 2012) and politics (US Presidential Elections 2012 and Super Tuesday 2012). Noting the peculiar nature of event dynamics in these two domains, we apply different time-windows and update rates to each of the datasets in order to study their impact on performance. The performance effectiveness results reveal that our approach is able to accurately detect and track newsworthy content. In addition, the results show that the adaptation of the time-window exhibits better performance especially on the sports dataset, which can be attributed to the usually shorter duration of football events
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