82 |
Acoustic features of dysphonic speech vs normal speech in New Zealand English speakers
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83 |
Modeling verb valency in a computational grammar for Portuguese in the HPSG formalism ; Modelação da valência verbal numa gramática computacional do português no formalismo HPSG
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In: Domínios de Lingu@gem; Ahead of Print; 1-63 ; 1980-5799 (2022)
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84 |
Causal and Semantic Relations in L2 Text Processing: An Eye-Tracking Study
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Nahatame, Shingo. - : University of Hawaii National Foreign Language Resource Center, 2022. : Center for Language & Technology, 2022
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85 |
Recognition of Urdu sign language: a systematic review of the machine learning classification
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In: PeerJ Comput Sci (2022)
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86 |
Multi-label emotion classification of Urdu tweets
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In: PeerJ Comput Sci (2022)
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87 |
Eine agentenbasierte Architektur für Programmierung mit gesprochener Sprache
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88 |
Word Frequency Analysis of Community Reaction to Religious Violence on Social Media
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In: School of Computer Science & Engineering Faculty Publications (2022)
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89 |
(Re)shaping online narratives: when bots promote the message of President Trump during his first impeachment
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In: PeerJ Comput Sci (2022)
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90 |
A systematic literature review on spam content detection and classification
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In: PeerJ Comput Sci (2022)
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Abstract:
The presence of spam content in social media is tremendously increasing, and therefore the detection of spam has become vital. The spam contents increase as people extensively use social media, i.e., Facebook, Twitter, YouTube, and E-mail. The time spent by people using social media is overgrowing, especially in the time of the pandemic. Users get a lot of text messages through social media, and they cannot recognize the spam content in these messages. Spam messages contain malicious links, apps, fake accounts, fake news, reviews, rumors, etc. To improve social media security, the detection and control of spam text are essential. This paper presents a detailed survey on the latest developments in spam text detection and classification in social media. The various techniques involved in spam detection and classification involving Machine Learning, Deep Learning, and text-based approaches are discussed in this paper. We also present the challenges encountered in the identification of spam with its control mechanisms and datasets used in existing works involving spam detection.
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Keyword:
Computational Linguistics
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URL: http://www.ncbi.nlm.nih.gov/pubmed/35174265 http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8802784/ https://doi.org/10.7717/peerj-cs.830
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91 |
People’s expectations and experiences of big data collection in the Saudi context
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In: PeerJ Comput Sci (2022)
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92 |
Developing and evaluating cybersecurity competencies for students in computing programs
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In: PeerJ Comput Sci (2022)
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93 |
Multitask Pointer Network for Multi-Representational Parsing
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94 |
CorpusExplorer ; Eine Software zur korpuspragmatischen Analyse
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95 |
Horse or pony? Visual Typicality and Lexical Frequency Affect Variability in Object Naming
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In: Proceedings of the Society for Computation in Linguistics (2022)
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96 |
Masked language models directly encode linguistic uncertainty
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In: Proceedings of the Society for Computation in Linguistics (2022)
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Learning Stress Patterns with a Sequence-to-Sequence Neural Network
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In: Proceedings of the Society for Computation in Linguistics (2022)
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98 |
Modeling human-like morphological prediction
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In: Proceedings of the Society for Computation in Linguistics (2022)
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99 |
The interaction between cognitive ease and informativeness shapes the lexicons of natural languages
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In: Proceedings of the Society for Computation in Linguistics (2022)
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100 |
What is so Plautine about Plautine Language? Computers and the Style of Early Latin Drama
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In: Peter Barrios-Lech (2022)
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