Reflecting the trends in the academic landscape of special education using probabilistic dynamic topic modeling

haq, Muhammad Inaam ul; Li, Qianmu; Hou, Jun

doi:10.1108/lht-12-2021-0441

Cited by 3 publications

(4 citation statements)

References 77 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…, 2019; Zhao et al. , 2022) and machine learning-based methods (Inaam ul haq et al. , 2022; Nicholls and Culpepper, 2021; Sun and Fichman, 2023).…”

Section: Methodsmentioning

confidence: 99%

“…Topics. Previous studies have implemented the classification of text topics through manual coding Hoffman et al, 2019;Zhao et al, 2022) and machine learning-based methods (Inaam ul haq et al, 2022;Nicholls and Culpepper, 2021;Sun and Fichman, 2023). Since it is unknown which topics existed in the misinformation microblogs in advance, Top2Vec, an unsupervised machine learning approach, was used to cluster the text topics.…”

Section: Data Collection and Preprocessingmentioning

confidence: 99%

See 1 more Smart Citation

Depth, breadth and structural virality: the influence of emotion, topic, authority and richness on misinformation spread

Meng,

Dai,

Zhao

et al. 2023

LHT

View full text Add to dashboard Cite

PurposeThis study aims to investigate the mechanism of the misinformation spread based on the elaboration likelihood model and the effects of four factors – emotion, topic, authority and richness – on the depth, breadth and structural virality of misinformation spread.Design/methodology/approachThe authors collected 2,514 misinformation microblogs and 142,006 reposts from Weibo, used deep learning methods to identify the emotions and topics of misinformation and extracted the structural characteristics of the spreading network using the network analysis method.FindingsResults show that misinformation has a smaller spread size and breadth than true news but has a similar spread depth and structural virality. The differential influence of emotions on the structural characteristics of misinformation propagation was found: sadness can promote the breadth of misinformation spread, anger can promote depth and disgust can promote depth and structural virality. In addition, the international topic, the number of followers, images and videos can significantly and positively influence the misinformation's spread size, depth, breadth and structural virality.Originality/valueThe influencing factors of the structural characteristics of misinformation propagation are clarified, which is helpful for the detection and management of misinformation.

show abstract

“…, 2019; Zhao et al. , 2022) and machine learning-based methods (Inaam ul haq et al. , 2022; Nicholls and Culpepper, 2021; Sun and Fichman, 2023).…”

Section: Methodsmentioning

confidence: 99%

Section: Data Collection and Preprocessingmentioning

confidence: 99%

Depth, breadth and structural virality: the influence of emotion, topic, authority and richness on misinformation spread

Meng,

Dai,

Zhao

et al. 2023

LHT

View full text Add to dashboard Cite

show abstract

“…Results indicate that Phase I (1999-2006) focused on human capital resources development, Phase II (2007II ( -2014 focused on technology transition in education and Phase III (2015III ( -2021 focused on vocational studies in higher education towards increasing the productivity of human resources via the implementation of technology transition. Readers may also be interested in reading about bibliometric studies on innovation and entrepreneurship education (Feng et al, 2023) and special education (Inaam ul haq et al, 2023). Please also note some recent educational developments in a recent special issue (Chen et al, 2023).…”

Section: Educationmentioning

confidence: 99%

Editorial: Special selection on current bibliometrics and reviews

Chiu,

2024

LHT

View full text Add to dashboard Cite

Recently, we have started selecting articles of similar themes for regular issues and writing editorials to guide readers better to the Library Hi Tech and other related literature, such as "Bibliometrics and literature review" (Chiu and Ho, 2021, 2022a in 39(4), 40(3) and 41(2), "Information and Knowledge Management" (Chiu and Ho, 2023c) in 41(4), "Digital culture and reading" (Chiu and Ho, 2022b) in 40( 5), "Social Media and Social Network Research" (Chiu and Ho, 2023b) in 41(3), "Advances in Learning Technologies" (Chiu and Ho, 2023d) in 41( 6) and "40th anniversary: contemporary library research" (Chiu and Ho, 2022c) in 40( 6). In this issue, we select 15 current bibliometrics and reviews that should be interesting to the broad readers of Library Hi Tech.

show abstract

“…As for using bibliometrics technology in learning, Inaam ul haq et al (2023) reflected the trends in the academic landscape of special education using probabilistic dynamic topic modeling. They identified the hottest topic of research in this domain, including children's communication and speech using gaming techniques, mental retardation, cost effect on infant birth, involvement of special education children and their families, assistive technology information for special education, syndrome epilepsy and the impact of group study on skill development peers or self.…”

mentioning

confidence: 99%