860 research outputs found
False News On Social Media: A Data-Driven Survey
In the past few years, the research community has dedicated growing interest
to the issue of false news circulating on social networks. The widespread
attention on detecting and characterizing false news has been motivated by
considerable backlashes of this threat against the real world. As a matter of
fact, social media platforms exhibit peculiar characteristics, with respect to
traditional news outlets, which have been particularly favorable to the
proliferation of deceptive information. They also present unique challenges for
all kind of potential interventions on the subject. As this issue becomes of
global concern, it is also gaining more attention in academia. The aim of this
survey is to offer a comprehensive study on the recent advances in terms of
detection, characterization and mitigation of false news that propagate on
social media, as well as the challenges and the open questions that await
future research on the field. We use a data-driven approach, focusing on a
classification of the features that are used in each study to characterize
false information and on the datasets used for instructing classification
methods. At the end of the survey, we highlight emerging approaches that look
most promising for addressing false news
MEMEX: Detecting Explanatory Evidence for Memes via Knowledge-Enriched Contextualization
Memes are a powerful tool for communication over social media. Their affinity
for evolving across politics, history, and sociocultural phenomena makes them
an ideal communication vehicle. To comprehend the subtle message conveyed
within a meme, one must understand the background that facilitates its holistic
assimilation. Besides digital archiving of memes and their metadata by a few
websites like knowyourmeme.com, currently, there is no efficient way to deduce
a meme's context dynamically. In this work, we propose a novel task, MEMEX -
given a meme and a related document, the aim is to mine the context that
succinctly explains the background of the meme. At first, we develop MCC (Meme
Context Corpus), a novel dataset for MEMEX. Further, to benchmark MCC, we
propose MIME (MultImodal Meme Explainer), a multimodal neural framework that
uses common sense enriched meme representation and a layered approach to
capture the cross-modal semantic dependencies between the meme and the context.
MIME surpasses several unimodal and multimodal systems and yields an absolute
improvement of ~ 4% F1-score over the best baseline. Lastly, we conduct
detailed analyses of MIME's performance, highlighting the aspects that could
lead to optimal modeling of cross-modal contextual associations.Comment: 9 pages main + 1 ethics + 3 pages ref. + 4 pages app (Total: 17
pages
A Comprehensive Survey of Deep Learning in Remote Sensing: Theories, Tools and Challenges for the Community
In recent years, deep learning (DL), a re-branding of neural networks (NNs),
has risen to the top in numerous areas, namely computer vision (CV), speech
recognition, natural language processing, etc. Whereas remote sensing (RS)
possesses a number of unique challenges, primarily related to sensors and
applications, inevitably RS draws from many of the same theories as CV; e.g.,
statistics, fusion, and machine learning, to name a few. This means that the RS
community should be aware of, if not at the leading edge of, of advancements
like DL. Herein, we provide the most comprehensive survey of state-of-the-art
RS DL research. We also review recent new developments in the DL field that can
be used in DL for RS. Namely, we focus on theories, tools and challenges for
the RS community. Specifically, we focus on unsolved challenges and
opportunities as it relates to (i) inadequate data sets, (ii)
human-understandable solutions for modelling physical phenomena, (iii) Big
Data, (iv) non-traditional heterogeneous data sources, (v) DL architectures and
learning algorithms for spectral, spatial and temporal data, (vi) transfer
learning, (vii) an improved theoretical understanding of DL systems, (viii)
high barriers to entry, and (ix) training and optimizing the DL.Comment: 64 pages, 411 references. To appear in Journal of Applied Remote
Sensin
An Attention-Based Model for Predicting Contextual Informativeness and Curriculum Learning Applications
Both humans and machines learn the meaning of unknown words through
contextual information in a sentence, but not all contexts are equally helpful
for learning. We introduce an effective method for capturing the level of
contextual informativeness with respect to a given target word. Our study makes
three main contributions. First, we develop models for estimating contextual
informativeness, focusing on the instructional aspect of sentences. Our
attention-based approach using pre-trained embeddings demonstrates
state-of-the-art performance on our single-context dataset and an existing
multi-sentence context dataset. Second, we show how our model identifies key
contextual elements in a sentence that are likely to contribute most to a
reader's understanding of the target word. Third, we examine how our contextual
informativeness model, originally developed for vocabulary learning
applications for students, can be used for developing better training curricula
for word embedding models in batch learning and few-shot machine learning
settings. We believe our results open new possibilities for applications that
support language learning for both human and machine learner
PCROD: Context Aware Role based Offensive Detection using NLP/ DL Approaches
With the increased use of social media many people misuse online platforms by uploading offensive content and sharing the same with vast audience. Here comes controlling of such offensive contents. In this work we concentrate on the issue of finding offensive text in social media. Existing offensive text detection systems treat weak pejoratives like ‘idiot‘ and extremely indecent pejoratives like ‘f***‘ as same as offensive irrespective of formal and informal contexts . In fact the weakly pejoratives in informal discussions among friends are casual and common which are not offensive but the same can be offensive when expressed in formal discussions. Crucial challenges to accomplish the task of role based offensive detection in text are i) considering the roles while classifying the text as offensive or not i) creating a contextual datasets including both formal and informal roles. To tackle the above mentioned challenges we develop deep neural network based model known as context aware role based offensive detection(CROD). We examine CROD on the manually created dataset that is collected from social networking sites. Results show that CROD gives better performance with RoBERTa with an accuracy of 94% while considering the context and role in data specifics
- …