I am an assistant professor in the Philip Merrill College of Journalism and the College of Information Studies of the University of Maryland, College Park. I have interests in research areas related to Big Data and Data Science, including Database, Data Mining, and Natural Language Processing. My current research focus is on Computational Journalism and Social Sensing. I am directing the Computational Journalism Lab at UMD. Before joining UMCP, I was an assistant professor in the Computer and Information Science department at the University of Mississippi. I earned a doctoral degree in Computer Science from University of Texas at Arlington in 2016.

Research Projects

For recent research projects, please visit the CJLab's webpage.

Computational Journalism
  1. Automated Fact-checking: Politicians and media figures make claims about “facts” all the time. The new army of fact-checkers can often expose claims which are false, exaggerated or half-truths. Technology, social media and new forms of journalism have made it easier than ever to disseminate falsehoods and half-truths faster than the fact-checkers can expose them. This “gap” in time and availability limits the effectiveness of fact-checking. The goal of this project is to pursue towards a completely automatic fact-checking platform, investigate the technical challenges and propose potential solutions [C+J 2015]. We are building ClaimBuster [CIKM 2015], a platform to monitor live streams, websites, and social media to catch factual claims, detect matches with a curated repository of fact-checks, and deliver the matches instantly to viewers. Major components of the platform are- text mining, social media analysis and collaborative fact-checking. This project has received media attention from multiple news outlets, including the guardian, Austin American-Statesman, Poynter and New Scientist.

  2. Significant Fact Monitoring: The goal of this project is to augment journalists identify data-backed, attention-seizing facts which serve as leads to news stories. Examples of such facts are- “This month the Chinese capital has experienced 10 days with a maximum temperature in around 35 degrees Celsius—the most for the month of July in a decade”, “Michael Jordan had 53 points in the Chicago Bulls' win over the Detroit Pistons. No one before had a better or equal performance in 1995-96 season”. Given an append-only database, upon the arrival of a new tuple, the challenge is to design algorithms which efficiently search for facts without exhaustively testing all possible ones [ICDE 2014, C+J 2014]. We developed FactWatcher [VLDB 2014], a system which finds story leads from ever-growing data and provides features including fact ranking, fact-to-statement translation, and keyword-based fact search. This system won an Excellent Demonstration Award in VLDB 2014.

Multi-dimensional Pareto-optimal Analysis
  1. Skyline Group: Traditional Pareto frontier (skyline) computation is inadequate to answer queries which need to analyze not only individual points but also groups of points. To approach this gap, we proposed a novel concept “Skyline Group” [TKDE 2014, CIKM 2012] that represents groups which are not dominated by any other groups. We demonstrated its applications through a web-based system CrewScout [CIKM 2014] in question answering, expert team formation and paper reviewer selection. An attractive characteristic of a skyline team is that no other team of equal size can dominate it. In contrast, given a non-skyline team, there is always a better skyline team. This property distinguishes CrewScout from other team recommendation techniques.

  2. Crowdsourcing Pareto-optimal Objects: Finding Pareto-optimal objects through crowdsourcing has applications in public opinion collection, group decision making, and information exploration. Departing from prior studies on crowdsourcing skyline and ranking queries, it considers the case where objects do not have explicit attributes and preference relations on objects are strict partial orders. The partial orders are derived by aggregating crowdsourcers’ responses to pairwise comparison questions. The goal is to find all Pareto-optimal objects by the fewest possible questions [CIKM 2015].

University of Maryland
JOUR 779V/479V, INST 408I: Computational Journalism FALL 2019
INST 627: Data Analytics for Information Professionals FALL 2020
INST 767: Big Data Analysis SPRING 2020, 2021
University of Mississippi
Engr 691: Advanced Topics in NLP FALL 2018
Csci 582: Computational Journalism SPRING 2019, SPRING 2017
Csci 581: Data Mining SPRING 2018, FALL 2016
Csci 517: Natural Language Processing FALL 2018
Csci 444: Information Visualization FALL 2017
Csci 387: Software Design and Development SPRING 2019, SPRING 2018, SPRING 2017
University of Texas at Arlington [primarily as a Teaching Assistant]
CSE 6324: Advanced Topics in Software Engineering SPRING 2014
CSE 5311: Design and Analysis of Algorithms FALL 2013, FALL 2011
CSE 4334/5334: Data Mining FALL 2014 [Course Instructor]
CSE 3330: Database Systems and File Structures SPRING 2012, SUMMER 2011, SPRING 2011, FALL 2010
CSE 1310: Introduction to Computers and Programming SPRING 2011
Daffodil International University
Computer Fundamentals, Numerical Methods, Instrumentation and Control, Electrical Circuit, Compiler, Simulation and Modeling, VLSI.
  1. For recent publications, please visit the CJLab's webpage.
  2. Naeemul Hassan, Fatma Arslan, Chengkai Li, and Mark Tremayne. Toward Automated Fact-Checking: Detecting Check-worthy Factual Claims by ClaimBuster. In Proceedings of the 23rd SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2017), 10 pages, Halifax, Nova Scotia, Canada, August 2017. [Paper]
  3. Naeemul Hassan, Gensheng Zhang, Fatma Arslan, Josue Caraballo, Damian Jimenez, Siddhant Gawsane, Shohedul Hasan, Minumol Joseph, Aaditya Kulkarni, Anil Kumar Nayak, Vikas Sable, Chengkai Li, Mark Tremayne. ClaimBuster: The First-ever End-to-end Fact-checking System. In Proceedings of the 43rd International Conference on Very Large Databases (VLDB 2017), 4 pages, Munich, Germany, August 2017. [Paper]
  4. Naeemul Hassan, Mark Tremayne, Fatma Arslan and Chengkai Li. Comparing Automated Factual Claim Detection Against Judgments of Journalism Organizations. In Proceedings of the 2016 Computation+Journalism Symposium, 5 pages, San Francisco, CA, USA, September 2016. [Paper] [Presentation]
  5. Naeemul Hassan, Chengkai Li, and Mark Tremayne. Detecting Check-worthy Factual Claims in Presidential Debates. In Proceedings of the 24th ACM International Conference on Information and Knowledge Management (CIKM), pages 1835-1838, Melbourne, Australia, October 2015. DOI 10.1145/2806416.2806652. [Paper] [Poster]
  6. Abolfazl Asudeh, Gensheng Zhang, Naeemul Hassan, Chengkai Li, and Gergely Zaruba. Crowdsourcing Pareto-Optimal Object Finding by Pairwise Comparisons. In Proceedings of the 24th ACM International Conference on Information and Knowledge Management (CIKM), pages 753-762, Melbourne, Australia, October 2015. DOI 10.1145/2806416.2806451. [Paper]
  7. Naeemul Hassan, Bill Adair, James Hamilton, Chengkai Li, Mark Tremayne, Jun Yang and Cong Yu. The Quest to Automate Fact-Checking. In Proceedings of the 2015 Computation+Journalism Symposium, 5 pages, New York City, USA, October 2015. [Paper]
  8. Naeemul Hassan, Huadong Feng, Ramesh Venkataraman, Gautam Das, Chengkai Li, and Nan Zhang. Anything You Can Do, I Can Do Better: Finding Expert Teams by CrewScout. In Proceedings of the 23rd ACM International Conference on Information and Knowledge Management (CIKM), pages 2030-2032, Shanghai, China, November 2014. DOI 10.1145/2661829.2661839. [Paper] [Poster] [Demo]
  9. Naeemul Hassan, Afroza Sultana, You Wu, Gensheng Zhang, Chengkai Li, Jun Yang, and Cong Yu. Data In, Fact Out: Automated Monitoring of Facts by FactWatcher. In Proceedings of the VLDB Endowment (PVLDB), pages 1557-1560, Hangzhou, China, September 2014. DOI 10.14778/2733004.2733029. Excellent demonstration award. [Paper] [Poster] [Demo]
  10. Brett Walenz, You (Will) Wu, Seokhyun (Alex) Song, Emre Sonmez, Eric Wu, Kevin Wu, Pankaj K. Agarwal, Jun Yang, Naeemul Hassan, Afroza Sultana, Gensheng Zhang, Chengkai Li, and Cong Yu. Finding, Monitoring, and Checking Claims Computationally Based on Structured Data. In Proceedings of the 2014 Computation+Journalism Symposium, 5 pages, New York City, USA, October 2014. [Paper] [Presentation]
  11. Nan Zhang, Chengkai Li, Naeemul Hassan, Sundaresan Rajasekaran, and Gautam Das. On Skyline Groups. IEEE Transactions on Knowledge and Data Engineering (TKDE), 26(4):942-956, April 2014. DOI 10.1109/TKDE.2013.119. [Paper]
  12. Afroza Sultana, Naeemul Hassan, Chengkai Li, Jun Yang, and Cong Yu. Incremental Discovery of Prominent Situational Facts. In Proceedings of the 30th International Conference on Data Engineering (ICDE), pages 112-123, Chicago, Illinois, April 2014. DOI 10.1109/ICDE.2014.6816644. [Paper] [Presentation]
  13. Chengkai Li, Nan Zhang, Naeemul Hassan, Sundaresan Rajasekaran, and Gautam Das. On Skyline Groups. In Proceedings of the 21st ACM International Conference on Information and Knowledge Management (CIKM), pages 2119-2123, Maui, Hawaii, November 2012. DOI 10.1145/2396761.2398585. [Paper] [Presentation]
  14. Zamshed Iqbal Chowdhury, Masudul Haider Imtiaz, Muhammad Moinul Azam, Mst Sumi, Rumana Aktar, Md Rahman, Farzana Alam, Ishtiak Hussain and Naeemul Hassan. Design and Deployment of a Robust Remote River Level Sensor Network. IEEE Sensors Applications Symposium (SAS), pages 244-249, San Antonio, Texas, February 2011. DOI 10.1109/SAS.2011.5739776. [Paper]
  15. SM Rifat Ahsan, Mohammad Saiful Islam, Naeemul Hassan and Ashikur Rahman. Exploiting Packet Distribution for Tuning RTS Threshold in IEEE 802.11. IEEE 25th Biennial Symposium on Communications (QBSC), pages 369-372, Kingston, Ontario, Canada, May 2010. DOI 10.1109/BSC.2010.5472955. [Paper]
  16. SM Rifat Ahsan, Mohammad Saiful Islam, Naeemul Hassan and Ashikur Rahman. Packet Distribution Based Tuning of RTS Threshold in IEEE 802.11. IEEE Symposium on Computers and Communications (ISCC), pages 1-6, Riccione, Italy, June 2010. DOI 10.1109/ISCC.2010.5546703. [Paper]
Classification of Indoor and Outdoor Telecommunications Events of Mobile Telecommunications Networks. Mario Kosseifi, Roswell, GA (US); Mark Austin, Roswell, GA (US); Jeremy Fix, Acworth, GA (US); Naeemul Hassan, Arlington, TX (US); and Sheldon Kent Meredith, Marietta, GA (US). AT&T MOBILITY II LLC (Atlanta, GA, US). US20140155085 A1. December 4, 2012.
Email: ude.dmu@nassahn
Phone: 6294-504-103 1+
Curriculum Vitae
alt : CV_Naeemul_Hassan.pdf