Vian Sobeeh
Job title or function
Vian Sobeeh is a member of the ICCC alumni community. Documented research areas include fake-news detection, deep learning and topic modeling.
Selected published work: “Fake News Detection Through Topic Modeling and Optimized Deep Learning with Multi-Domain Knowledge Sources” (2021). Publication details and links are listed below.
Education
- Ph.D. in Computer Science and Informatics — Oakland University, 2020
Research statistics
Verified ICCC collection
- Documented research records
- 4
- Publication records
- 4
- Degree research records
- 0
Indexed citation figures
- Indexed research works
- 19
- Citations to indexed works · all years
- 101
- Citations received · 2024–2026
- 50
- 202417
- 202523
- 202610
Annual citations: 2024: 17 · 2025: 23 · 2026: 10. Retrieved 2026-10-24. Source 1Checked 2026-10-24
ICCC counts cover the verified collection. Citation figures cover only the displayed indexed source records, not guaranteed career totals. OpenAlex coverage and author clustering can be incomplete. These figures differ from Google Scholar and include self-citations. Compare available member metrics →
Research & Publications
4 research works · Newest first
Authored and co-authored publications, with authors shown in the source order. This list reflects the records currently verified by ICCC and may not be complete.
Fake News Detection Through Topic Modeling and Optimized Deep Learning with Multi-Domain Knowledge Sources
View PublicationOpenAlex: 9 citations Source ↗
Start here · New researcher guide
A reading workflow, not an author-verified summary. Confirm all details in the original publication.
- Check the fit. Read the abstract and introduction. Write down the research question and explain how it relates to your idea.
- Understand the method. Identify the approach, assumptions, data and evaluation measures in the paper.
- Check reproducibility. Look for linked code, datasets and supplementary material. Availability has not been verified by ICCC.
- Find your next question. Read limitations and future work. Propose one extension and check newer literature before claiming a research gap.
- Plan a first experiment. Define a baseline, the change you will test and a measurable outcome. Discuss feasibility with your supervisor.
For questions or collaboration, visit the member profile and use a listed contact channel. Authorship and collaboration require agreement.
Twitter Data Collection and Extraction
View PublicationOpenAlex: 7 citations Source ↗
Start here · New researcher guide
A reading workflow, not an author-verified summary. Confirm all details in the original publication.
- Check the fit. Read the abstract and introduction. Write down the research question and explain how it relates to your idea.
- Understand the method. Identify the approach, assumptions, data and evaluation measures in the paper.
- Check reproducibility. Look for linked code, datasets and supplementary material. Availability has not been verified by ICCC.
- Find your next question. Read limitations and future work. Propose one extension and check newer literature before claiming a research gap.
- Plan a first experiment. Define a baseline, the change you will test and a measurable outcome. Discuss feasibility with your supervisor.
For questions or collaboration, visit the member profile and use a listed contact channel. Authorship and collaboration require agreement.
Towards a New Indicator for Evaluating Universities Based on Twitter Sentiment Analysis
View PublicationOpenAlex: 8 citations Source ↗
Start here · New researcher guide
A reading workflow, not an author-verified summary. Confirm all details in the original publication.
- Check the fit. Read the abstract and introduction. Write down the research question and explain how it relates to your idea.
- Understand the method. Identify the approach, assumptions, data and evaluation measures in the paper.
- Check reproducibility. Look for linked code, datasets and supplementary material. Availability has not been verified by ICCC.
- Find your next question. Read limitations and future work. Propose one extension and check newer literature before claiming a research gap.
- Plan a first experiment. Define a baseline, the change you will test and a measurable outcome. Discuss feasibility with your supervisor.
For questions or collaboration, visit the member profile and use a listed contact channel. Authorship and collaboration require agreement.
Enhancing the Fake News Detection by Applying Effective Feature Selection Based on Semantic Sources
View PublicationOpenAlex: 22 citations Source ↗
Start here · New researcher guide
A reading workflow, not an author-verified summary. Confirm all details in the original publication.
- Check the fit. Read the abstract and introduction. Write down the research question and explain how it relates to your idea.
- Understand the method. Identify the approach, assumptions, data and evaluation measures in the paper.
- Check reproducibility. Look for linked code, datasets and supplementary material. Availability has not been verified by ICCC.
- Find your next question. Read limitations and future work. Propose one extension and check newer literature before claiming a research gap.
- Plan a first experiment. Define a baseline, the change you will test and a measurable outcome. Discuss feasibility with your supervisor.
For questions or collaboration, visit the member profile and use a listed contact channel. Authorship and collaboration require agreement.
No publications match your search.