3,480 research outputs found
EveTAR: Building a Large-Scale Multi-Task Test Collection over Arabic Tweets
This article introduces a new language-independent approach for creating a
large-scale high-quality test collection of tweets that supports multiple
information retrieval (IR) tasks without running a shared-task campaign. The
adopted approach (demonstrated over Arabic tweets) designs the collection
around significant (i.e., popular) events, which enables the development of
topics that represent frequent information needs of Twitter users for which
rich content exists. That inherently facilitates the support of multiple tasks
that generally revolve around events, namely event detection, ad-hoc search,
timeline generation, and real-time summarization. The key highlights of the
approach include diversifying the judgment pool via interactive search and
multiple manually-crafted queries per topic, collecting high-quality
annotations via crowd-workers for relevancy and in-house annotators for
novelty, filtering out low-agreement topics and inaccessible tweets, and
providing multiple subsets of the collection for better availability. Applying
our methodology on Arabic tweets resulted in EveTAR , the first
freely-available tweet test collection for multiple IR tasks. EveTAR includes a
crawl of 355M Arabic tweets and covers 50 significant events for which about
62K tweets were judged with substantial average inter-annotator agreement
(Kappa value of 0.71). We demonstrate the usability of EveTAR by evaluating
existing algorithms in the respective tasks. Results indicate that the new
collection can support reliable ranking of IR systems that is comparable to
similar TREC collections, while providing strong baseline results for future
studies over Arabic tweets
News’ Credibility Detection on Social Media Using Machine Learning Algorithms
Social media is essential in many aspects of our lives. Social media allows us to find news for free. anyone can access it easily at any time. However, social media may also facilitate the rapid spread of misleading news. As a result, there is a probability that low-quality news, including incorrect and fake information, will spread over social media. As well as detecting news credibility on social media becomes essential because fake news can affect society negatively, and the spread of false news has a considerable impact on personal reputation and public trust. In this research, we conducted a model that detects the credibility of Arabic news from social media; particularly Arabic tweets. The content of the tweets revolves around the COVID-19 pandemic. The proposed model applied to detect news credibility using text mining techniques and one of the well-known machine learning classifiers, Decision tree which has the best accuracy equal to 86.6
Critical Impact of Social Networks Infodemic on Defeating Coronavirus COVID-19 Pandemic: Twitter-Based Study and Research Directions
News creation and consumption has been changing since the advent of social
media. An estimated 2.95 billion people in 2019 used social media worldwide.
The widespread of the Coronavirus COVID-19 resulted with a tsunami of social
media. Most platforms were used to transmit relevant news, guidelines and
precautions to people. According to WHO, uncontrolled conspiracy theories and
propaganda are spreading faster than the COVID-19 pandemic itself, creating an
infodemic and thus causing psychological panic, misleading medical advises, and
economic disruption. Accordingly, discussions have been initiated with the
objective of moderating all COVID-19 communications, except those initiated
from trusted sources such as the WHO and authorized governmental entities. This
paper presents a large-scale study based on data mined from Twitter. Extensive
analysis has been performed on approximately one million COVID-19 related
tweets collected over a period of two months. Furthermore, the profiles of
288,000 users were analyzed including unique users profiles, meta-data and
tweets context. The study noted various interesting conclusions including the
critical impact of the (1) exploitation of the COVID-19 crisis to redirect
readers to irrelevant topics and (2) widespread of unauthentic medical
precautions and information. Further data analysis revealed the importance of
using social networks in a global pandemic crisis by relying on credible users
with variety of occupations, content developers and influencers in specific
fields. In this context, several insights and findings have been provided while
elaborating computing and non-computing implications and research directions
for potential solutions and social networks management strategies during crisis
periods.Comment: 11 pages, 10 figures, Journal Articl
A Model to Measure the Spread Power of Rumors
Nowadays, a significant portion of daily interacted posts in social media are
infected by rumors. This study investigates the problem of rumor analysis in
different areas from other researches. It tackles the unaddressed problem
related to calculating the Spread Power of Rumor (SPR) for the first time and
seeks to examine the spread power as the function of multi-contextual features.
For this purpose, the theory of Allport and Postman will be adopted. In which
it claims that there are two key factors determinant to the spread power of
rumors, namely importance and ambiguity. The proposed Rumor Spread Power
Measurement Model (RSPMM) computes SPR by utilizing a textual-based approach,
which entails contextual features to compute the spread power of the rumors in
two categories: False Rumor (FR) and True Rumor (TR). Totally 51 contextual
features are introduced to measure SPR and their impact on classification are
investigated, then 42 features in two categories "importance" (28 features) and
"ambiguity" (14 features) are selected to compute SPR. The proposed RSPMM is
verified on two labelled datasets, which are collected from Twitter and
Telegram. The results show that (i) the proposed new features are effective and
efficient to discriminate between FRs and TRs. (ii) the proposed RSPMM approach
focused only on contextual features while existing techniques are based on
Structure and Content features, but RSPMM achieves considerably outstanding
results (F-measure=83%). (iii) The result of T-Test shows that SPR criteria can
significantly distinguish between FR and TR, besides it can be useful as a new
method to verify the trueness of rumors
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