Page: 1 2 3 4 5 6 7 8 9... 1.020
| 82 |
Acoustic features of dysphonic speech vs normal speech in New Zealand English speakers
|
|
|
|
BASE
|
|
Show details
|
|
|
| 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
|
|
|
|
In: Domínios de Lingu@gem; Ahead of Print; 1-63 ; 1980-5799 (2022)
|
|
BASE
|
|
Show details
|
|
|
| 84 |
Causal and Semantic Relations in L2 Text Processing: An Eye-Tracking Study
|
|
Nahatame, Shingo. - : University of Hawaii National Foreign Language Resource Center, 2022. : Center for Language & Technology, 2022
|
|
BASE
|
|
Show details
|
|
|
| 85 |
Recognition of Urdu sign language: a systematic review of the machine learning classification
|
|
|
|
In: PeerJ Comput Sci (2022)
|
|
BASE
|
|
Show details
|
|
|
| 86 |
Multi-label emotion classification of Urdu tweets
|
|
|
|
In: PeerJ Comput Sci (2022)
|
|
BASE
|
|
Show details
|
|
|
| 87 |
Eine agentenbasierte Architektur für Programmierung mit gesprochener Sprache
|
|
|
|
BASE
|
|
Show details
|
|
|
| 88 |
Word Frequency Analysis of Community Reaction to Religious Violence on Social Media
|
|
|
|
In: School of Computer Science & Engineering Faculty Publications (2022)
|
|
Abstract:
Researchers in data science, psychology, and linguistics have recognized the value in assessing community reactions to traumatic experiences on social media. Twitter in particular, is popular for sharing opinions and discussing current events. In this work, we collect and analyze the reaction to three shootings at different houses of worship. Three events were chosen: the massacre at the Emanuel Church in Charleston, NC, the massacre at the Tree of Life Synagogue in Pittsburg, PA, and the massacres at two mosques in Christchurch, New Zealand. The events were all committed by shooters with a similar supremacist mentality. We used the python programming language to collect the data from twitter using popular hashtags for the events. We then cleaned it up by removing punctuation, web links, stop words, hashtags, and foreign letters. We then did a word frequency count on the remaining words, and generated word clouds for each event. We also did a search for well-known hate words in the corpus. We found out that the three events generate a different amount of chatter and people use very different words to describe each of them. The church dataset had the most varied set of popular words with the shooter’s name being the most used. The synagogue dataset had the least amount of tweets and two words dominating the discussion: “shooting” and “synagogue”. The mosque dataset had the least discussion about the shooter in comparison to the other events. On the positive side, very few hate tweets were found in any dataset.
|
|
Keyword:
Community Psychology; Community-Based Research; Computational Linguistics; Data Science; Religious violence; Social media; Word frequency
|
|
URL: https://link.springer.com/chapter/10.1007%2F978-3-030-80119-9_39
https://digitalcommons.sacredheart.edu/computersci_fac/159
|
|
BASE
|
|
Hide details
|
|
|
| 89 |
(Re)shaping online narratives: when bots promote the message of President Trump during his first impeachment
|
|
|
|
In: PeerJ Comput Sci (2022)
|
|
BASE
|
|
Show details
|
|
|
| 90 |
A systematic literature review on spam content detection and classification
|
|
|
|
In: PeerJ Comput Sci (2022)
|
|
BASE
|
|
Show details
|
|
|
| 91 |
People’s expectations and experiences of big data collection in the Saudi context
|
|
|
|
In: PeerJ Comput Sci (2022)
|
|
BASE
|
|
Show details
|
|
|
| 92 |
Developing and evaluating cybersecurity competencies for students in computing programs
|
|
|
|
In: PeerJ Comput Sci (2022)
|
|
BASE
|
|
Show details
|
|
|
| 93 |
Multitask Pointer Network for Multi-Representational Parsing
|
|
|
|
BASE
|
|
Show details
|
|
|
| 94 |
CorpusExplorer ; Eine Software zur korpuspragmatischen Analyse
|
|
|
|
BASE
|
|
Show details
|
|
|
| 95 |
Horse or pony? Visual Typicality and Lexical Frequency Affect Variability in Object Naming
|
|
|
|
In: Proceedings of the Society for Computation in Linguistics (2022)
|
|
BASE
|
|
Show details
|
|
|
| 96 |
Masked language models directly encode linguistic uncertainty
|
|
|
|
In: Proceedings of the Society for Computation in Linguistics (2022)
|
|
BASE
|
|
Show details
|
|
|
| 97 |
Learning Stress Patterns with a Sequence-to-Sequence Neural Network
|
|
|
|
In: Proceedings of the Society for Computation in Linguistics (2022)
|
|
BASE
|
|
Show details
|
|
|
| 98 |
Modeling human-like morphological prediction
|
|
|
|
In: Proceedings of the Society for Computation in Linguistics (2022)
|
|
BASE
|
|
Show details
|
|
|
| 99 |
The interaction between cognitive ease and informativeness shapes the lexicons of natural languages
|
|
|
|
In: Proceedings of the Society for Computation in Linguistics (2022)
|
|
BASE
|
|
Show details
|
|
|
| 100 |
What is so Plautine about Plautine Language? Computers and the Style of Early Latin Drama
|
|
|
|
In: Peter Barrios-Lech (2022)
|
|
BASE
|
|
Show details
|
|
|
Page: 1 2 3 4 5 6 7 8 9... 1.020 |
|
