Visual User-Generated Content Verification in Journalism: An Overview

Khan, Sohail Ahmed; Sheikhi, Ghazaal; Opdahl, Andreas L.; Rabbi, Fazle; Stoppel, Sergej; Trattner, Christoph; Dang-Nguyen, Duc-Tien

doi:10.1109/access.2023.3236993

Cited by 8 publications

(2 citation statements)

References 108 publications

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“…As previously highlighted by Yusnanto and Wahyudiono (2023) and Basri and Siam (2019), social media is an online space that makes it possible for users to construct a profile that is publicly accessible or semi-public within a particular system, communicate with a list of other users within the framework, and at the same time allow other users to observe and intersect with one another within the same system. Social media is a technology that offers extremely interactive environments via which people from various backgrounds can share, co-create, discuss, and edit usergenerated content by employing mobile and web-based technologies (Khan et al, 2023). It is believed that SMPs that include but are not limited to, LinkedIn, YouTube, Facebook, Twitter, Research Gate, and Google+ can be used as a means of instruction for teaching university courses (Amara et al, 2023;Adeola et al, 2022).…”

Section: Literature Reviewmentioning

confidence: 99%

Revolutionizing education: unleashing the power of social media in Saudi Arabian public universities

Alshammari,

Al-Mamary,

Abubakar

2024

Humanit Soc Sci Commun

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This study examines the influence of social media on learning and education inside higher education institutions in Saudi Arabia. This study attempts to utilize the e-learning acceptance model (e-LAM) in order to identify some of the variables that impact Saudi students’ inclination to utilize social media for educational purposes at higher learning institutions in Saudi Arabia. The e-LAM draws on a number of different theoretical viewpoints in order to present a more comprehensive picture of the variables that encourage the use of social media in learning and education in Saudi Arabian higher education institutions while students are enrolled in universities. The study’s survey was completed by 369 undergraduates’ participants enrolled in various academic programs and different educational levels. With the use of structural equation modeling, the analysis was carried out in order to investigate the correlations that existed between the variables that were stated in the conceptual model. Social media adoption intentions in Saudi Arabian higher education institutions were found to be significantly related to performance and self, supporting hypotheses 3 and 4, while communication functionality (COM) and effort (E) were found to be not significant towards Saudi Arabian students’ intentions to adopt social media, thus rejecting hypotheses 1 and 2.

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Section: Literature Reviewmentioning

confidence: 99%

Revolutionizing education: unleashing the power of social media in Saudi Arabian public universities

Alshammari,

Al-Mamary,

Abubakar

2024

Humanit Soc Sci Commun

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“…Tajrian et al [40] offered a comprehensive review of methodologies for fake news analysis, providing a valuable synthesis of the current state of research in this field. Khan et al [41] focused on visual user-generated content verification in journalism, reflecting the growing importance of visual content in the realm of fake news. Fu et al [42] examined rumor spreading models considering the roles of online social networks and information overload, providing insights into the behavioral aspects of misinformation dissemination operations.…”

Section: Related Studymentioning

confidence: 99%

MSCMGTB: A Novel Approach for Multimodal Social Media Content Moderation Using Hybrid Graph Theory and Bio-Inspired Optimization

Arya,

Pandey,

Gopal Krishna Patro

et al. 2024

IEEE Access

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In an era where social media platforms burgeon with diverse content, effective content moderation becomes imperative to filter harmful materials. Traditional methods often grapple with the dual challenges of accuracy and computational efficiency levels. These conventional approaches typically rely on either text-based or image-based analysis, neglecting the complex interplay of multimodal content prevalent in social media scenarios. This limitation leads to suboptimal content filtering, often missing contextually nuanced or visually deceptive harmful content sets. Addressing these challenges, the proposed work introduces an innovative, hybrid deep learning model that integrates Convolutional Neural Networks (CNNs) and Transformers for nuanced visual content extraction from social media posts. CNNs adeptly handle image features, while Transformers enrich the analysis with contextual textual understanding, thereby enhancing accuracy in identifying harmful contents. Additionally, a Bi-directional Attention Mechanism (BAM) is employed for efficient text-visual fusion, dynamically prioritizing relevant information and reducing computational loads. The fusion process is further refined using Genetic Algorithms (GAs) for hyperparameter optimization, streamlining the model's performance levels. The model's robustness is demonstrated through its ability to discern complex intra and inter-modal relationships within social media content, facilitated by Graph Neural Networks (GNNs). Moreover, the model's scalability and deployment efficiency on social media platforms are ensured through quantization and pruning techniques, balancing accuracy with computational practicality levels. Empirical testing on datasets from Google, Facebook, and Kaggle reveals the model's superior performance over existing methods, with notable improvements in precision, accuracy, recall, AUC, specificity, and response delay in detecting harmful contents. The model also excels in preempting potential harmful content posters, offering enhanced pre-emption metrics.

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