1,375 research outputs found
The Media Bias Taxonomy: A Systematic Literature Review on the Forms and Automated Detection of Media Bias
The way the media presents events can significantly affect public perception,
which in turn can alter people's beliefs and views. Media bias describes a
one-sided or polarizing perspective on a topic. This article summarizes the
research on computational methods to detect media bias by systematically
reviewing 3140 research papers published between 2019 and 2022. To structure
our review and support a mutual understanding of bias across research domains,
we introduce the Media Bias Taxonomy, which provides a coherent overview of the
current state of research on media bias from different perspectives. We show
that media bias detection is a highly active research field, in which
transformer-based classification approaches have led to significant
improvements in recent years. These improvements include higher classification
accuracy and the ability to detect more fine-granular types of bias. However,
we have identified a lack of interdisciplinarity in existing projects, and a
need for more awareness of the various types of media bias to support
methodologically thorough performance evaluations of media bias detection
systems. Concluding from our analysis, we see the integration of recent machine
learning advancements with reliable and diverse bias assessment strategies from
other research areas as the most promising area for future research
contributions in the field
Reputation Systems of Online Communities Establishing a Research Agenda
Although online communities make it possible for a far greater number of participants to interact on the Web, there are challenges in creating mechanisms that reveal reputations for participants. Reputation Systems provide a proxy that establishes trust in e-commerce communities, social communities, and social news communities. There remain questions as to how reputation systems can be more widely used in online communities without damaging user confidence because participants have strong privacy expectations. This paper will review reputation systems in online communities, examine types, properties, and issues of reputation systems, survey the use of social networks and reputation systems in popular online communities, and present a research agenda to address issues of reputation systems
Control of Large Swarms via Random Finite Set Theory
Controlling large swarms of robotic agents has many challenges including, but
not limited to, computational complexity due to the number of agents,
uncertainty in the functionality of each agent in the swarm, and uncertainty in
the swarm's configuration. This work generalizes the swarm state using Random
Finite Set (RFS) theory and solves the control problem using model predictive
control which naturally handles the challenges. This work uses information
divergence to define the distance between swarm RFS and a desired distribution.
A stochastic optimal control problem is formulated using a modified L2
distance. Simulation results show that swarm densities converge to a target
destination, and the RFS control formulation can vary in the number of target
destinations
TaCL: Improving BERT Pre-training with Token-aware Contrastive Learning
Masked language models (MLMs) such as BERT and RoBERTa have revolutionized
the field of Natural Language Understanding in the past few years. However,
existing pre-trained MLMs often output an anisotropic distribution of token
representations that occupies a narrow subset of the entire representation
space. Such token representations are not ideal, especially for tasks that
demand discriminative semantic meanings of distinct tokens. In this work, we
propose TaCL (Token-aware Contrastive Learning), a novel continual pre-training
approach that encourages BERT to learn an isotropic and discriminative
distribution of token representations. TaCL is fully unsupervised and requires
no additional data. We extensively test our approach on a wide range of English
and Chinese benchmarks. The results show that TaCL brings consistent and
notable improvements over the original BERT model. Furthermore, we conduct
detailed analysis to reveal the merits and inner-workings of our approach.Comment: Camera-ready for NAACL 202
Server‐side workflow execution using data grid technology for reproducible analyses of data‐intensive hydrologic systems
Many geoscience disciplines utilize complex computational models for advancing understanding and sustainable management of Earth systems. Executing such models and their associated data preprocessing and postprocessing routines can be challenging for a number of reasons including (1) accessing and preprocessing the large volume and variety of data required by the model, (2) postprocessing large data collections generated by the model, and (3) orchestrating data processing tools, each with unique software dependencies, into workflows that can be easily reproduced and reused. To address these challenges, the work reported in this paper leverages the Workflow Structured Object functionality of the Integrated Rule‐Oriented Data System and demonstrates how it can be used to access distributed data, encapsulate hydrologic data processing as workflows, and federate with other community‐driven cyberinfrastructure systems. The approach is demonstrated for a study investigating the impact of drought on populations in the Carolinas region of the United States. The analysis leverages computational modeling along with data from the Terra Populus project and data management and publication services provided by the Sustainable Environment‐Actionable Data project. The work is part of a larger effort under the DataNet Federation Consortium project that aims to demonstrate data and computational interoperability across cyberinfrastructure developed independently by scientific communities.Plain Language SummaryExecuting computational workflows in the geosciences can be challenging, especially when dealing with large, distributed, and heterogeneous data sets and computational tools. We present a methodology for addressing this challenge using the Integrated Rule‐Oriented Data System (iRODS) Workflow Structured Object (WSO). We demonstrate the approach through an end‐to‐end application of data access, processing, and publication of digital assets for a scientific study analyzing drought in the Carolinas region of the United States.Key PointsReproducibility of data‐intensive analyses remains a significant challengeData grids are useful for reproducibility of workflows requiring large, distributed data setsData and computations should be co‐located on servers to create executable Web‐resourcesPeer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/137520/1/ess271_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/137520/2/ess271.pd
Voices' inter-animation detection with ReaderBench. Modelling and assessing polyphony in CSCL chats as voice synergy
International audienceStarting from dialogism in which every act is perceived as a dialogue, we shift the perspective towards multi-participant chat conversations from Computer Supported Collaborative Learning in which ideas, points of view or more generally put voices interact, inter-animate and generate the context of a conversation. Within this perspective of discourse analysis, we introduce an implemented framework, ReaderBench, for modeling and automatically evaluating polyphony that emerges as an overlap or synergy of voices. Moreover, multiple evaluation factors were analyzed for quantifying the importance of a voice and various functions were experimented to best reflect the synergic effect of co- occurring voices for modeling the underlying discourse structure
Voices' inter-animation detection with ReaderBench. Modelling and assessing polyphony in CSCL chats as voice synergy
International audienceStarting from dialogism in which every act is perceived as a dialogue, we shift the perspective towards multi-participant chat conversations from Computer Supported Collaborative Learning in which ideas, points of view or more generally put voices interact, inter-animate and generate the context of a conversation. Within this perspective of discourse analysis, we introduce an implemented framework, ReaderBench, for modeling and automatically evaluating polyphony that emerges as an overlap or synergy of voices. Moreover, multiple evaluation factors were analyzed for quantifying the importance of a voice and various functions were experimented to best reflect the synergic effect of co- occurring voices for modeling the underlying discourse structure
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