Sajjad Dadkhah
Biographic Data
| ID | 10054043 |
|---|---|
| NAME | Sajjad Dadkhah |
| GIVEN NAMES | Sajjad |
| FAMILY NAME | Dadkhah |
| SIGNATURE | DADKHAH S |
| AFFILIATIONS | University of New Brunswick |
| ORCID | 0000-0002-5582-0255 |
| VERIFIED | Yes |
| TOTAL WORKS | 3 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 3 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2024 |
| LATEST PUBLICATION YEAR | 2024 |
| H-INDEX | 0 |
MEFaND
Alongside social media platforms’ rise in popularity, fake news circulation has increased, highlighting the need for more practical methods to detect this phenomenon. The constantly evolving format of fake news makes it difficult for approaches that rely on a single modality of news to generalize the different types of false news. Furthermore, earlier approaches require extensive propagation data to determine the veracity of news, which can be ch…
Multimodal Fake News Analysis Based on Image–Text Similarity
With the fast and extensive development of computer vision techniques, multimodal analyses are utilized more frequently for online fake news detection. To better understand the image–text relationship and its role in fake news detection, in this article, we proposed and evaluated four image–text similarities, namely, textual similarity, semantic similarity, contextual similarity, and post-training similarity. The textual and semantic similarities…
The Largest Social Media Ground-Truth Dataset for Real/Fake Content
Automatic detection of fake content in social media such as Twitter is an enduring challenge. Technically, determining fake news on social media platforms is a straightforward binary classification problem. However, manually fact-checking even a small fraction of daily tweets would be nearly impossible due to the sheer volume. To address this challenge, we crawled and crowd-sourced one of the most extensive ground-truth tweet datasets. Utilizing …
No prominent works on this page.
MEFaND
Alongside social media platforms’ rise in popularity, fake news circulation has increased, highlighting the need for more practical methods to detect this phenomenon. The constantly evolving format of fake news makes it difficult for approaches that rely on a single modality of news to generalize the different types of false news. Furthermore, earlier approaches require extensive propagation data to determine the veracity of news, which can be ch…
Multimodal Fake News Analysis Based on Image–Text Similarity
With the fast and extensive development of computer vision techniques, multimodal analyses are utilized more frequently for online fake news detection. To better understand the image–text relationship and its role in fake news detection, in this article, we proposed and evaluated four image–text similarities, namely, textual similarity, semantic similarity, contextual similarity, and post-training similarity. The textual and semantic similarities…
The Largest Social Media Ground-Truth Dataset for Real/Fake Content
Automatic detection of fake content in social media such as Twitter is an enduring challenge. Technically, determining fake news on social media platforms is a straightforward binary classification problem. However, manually fact-checking even a small fraction of daily tweets would be nearly impossible due to the sheer volume. To address this challenge, we crawled and crowd-sourced one of the most extensive ground-truth tweet datasets. Utilizing …
Computer Science (3 works) · Misinformation and Its Impacts (3 works) · Advanced Malware Detection Techniques (2 works) · Artificial Intelligence (2 works) · Information retrieval (2 works) · Natural language processing (2 works) · Spam and Phishing Detection (2 works) · Cluster analysis (1 work) · Computer security (1 work) · Credibility (1 work)