DC11: Cross-lingual claim detection for fact-checking

Open Doctoral Candidate/PhD Position at Queen Mary University of London (QMUL), United Kingdom, for the HYBRIDS project


Reference number: HYBRIDS- DC11

PhD research topic: Cross-lingual claim detection for fact-checking

Host institution: Queen Mary University of London (QMUL), United Kingdom

PhD Enrolment: Queen Mary University of London (QMUL), United Kingdom

Main Supervisor: Dr. Arkaitz Zubiaga, Queen Mary University of London (QMUL), a.zubiaga@qmul.ac.uk

Co-supervisor: Dr. Rubén Miguez, Newtral Media Audiovisual (NEWTRAL)

Inter-sectoral Supervisors: Ms. Vindhya Singh and Ms. Stephanie Öttl, Industrieanlagen- Betriebsgesellschaft (IABG)


  1. To design a strategy to detect potentially verifiable sentences, also known as claims; this is the first task before producing an informed assessment of the veracity of the claim, known as fact-checking;
  2. To define annotation criteria for claim detection and collect a multilingual dataset of claims annotated with those criteria within the political domain;
  3. To use cross-lingual methods to deal with multilingual data;
  4. To cluster together related claims, including potential contradictions, which further informs the fact-checking process and helps quantify the popularity of a claim.

Expected outcomes:

  1. Development of a cross-lingual system aimed at detecting and clustering multilingual claims, as a first step both to assist fact-checkers in the selection process and to feed an automated fact-checking system.
  2. Elaboration of a multilingual dataset of political claims labelled according to the criteria considered in the previous analysis.
  3. Use cases on climate emergency and health.

Planned secondments:

Eligibility Criteria:

  • Mobility: At the time of recruitment, the researcher must not have resided or carried out his/her main activity (work, studies, etc.) in the United Kingdom for more than 12 months in the 36 months immediately before the recruitment date. Time spent as part of a procedure for obtaining refugee status under the Geneva Convention or compulsory national service are not taken into account.
  • The candidate must be at the date of recruitment a doctoral candidate (i.e. not already in possession of doctoral degree). Researchers who have successfully defended their doctoral thesis but who have not yet formally been awarded the doctoral degree will not be considered eligible.
  • The candidate must agree to work exclusively for the action.
Specific requirements:
  • Degree: All applicants should have a first-class honour degree or equivalent, or a MSc degree, in Computer Science (or a related discipline).
  • Programming skills: Excellent programming skills, ideally in python.
  • Language: Excellent command of English, together with good academic writing and presentation skills. The candidate must meet QMUL’s English language requirements: typically an Academic IELTS certificate with a minimum 6.5 overall score, and a minimum of 6.0 in writing and a minimum of 5.5 in reading, speaking and listening. For alternatives to IELTS, see: https://www.qmul.ac.uk/international-students/englishlanguagerequirements/postgraduateresearch/ There are certain circumstances where you may not be required to have an  English Language test to prove your proficiency.  If you hold a degree  from a majority English speaking country or your degree has been taught  and examined in English, please read: https://www.qmul.ac.uk/international-students/englishlanguagerequirements/postgraduatetaught/#d.en.622499

Desirable skills: Excellent knowledge of data science methods, experience with deep learning methods and working with large datasets. Experience in natural language processing is also a plus.

Estimated starting date: 1st July 2023

Contract: Full-time contract

Duration: 36 months, including 4 secondments of 2/3 months each, at other consortium members’ premises (see Secondment section)

Salary: approx. £3,765 (with family allowance) and £3,344 (without family allowance) subject to the funder’s euro / pound sterling exchange rates

Application Documents:

  • Europass CV (template available in the following link), including the names and contact details of two academic references, in English, highlighting the merits that are established as evaluation criteria;
  • Scans of Bachelor’s and Master’s transcripts, with certified translation in English (if the degree qualification is not in English); If you have not yet completed your master’s, you must submit a provisional academic transcript.
  • A motivation letter in English, highlighting the consistency between your profile and the chosen DC position/s for which you are applying and describing why you wish to be aHYBRIDS Doctoral Candidate to carry out a PhD; (max. 700 words)
  • Scanned copy of your ID card, resident’s card or passport currently in force;
  • Proof of excellent command of English, typically an IELTS certificate, but others are also accepted (see above). This is not required in case of native English speakers (i.e., English is your mother tongue).

In addition, you can add any other documents which you find relevant for the applications such as Master thesis, publications or project reports.

Evaluation criteria:

  • Academic background (up to 40 points)
  • Knowledge and specific achievements (up to 35 points)
  • Shortlisted candidates will be invited for an interview in which the selection committee will assess the applicant’s communication skills, initiative, and motivation to pursue a PhD. (up to 25 points)

Deadline: April 26, 2023, at 23h59 CET (UCT + 01:00)

Queen Mary’s commitment to our diverse and inclusive community is embedded in our appointments processes. Reasonable adjustments will be made at each stage of the recruitment process for any candidate with a disability.


Candidates are encouraged to contact the HYBRIDS Project Manager (info@hybridsproject.eu) for assistance or for any information related to the application process. When contacting, please indicate the position reference in the subject line.

Enquiries about research content must be sent to the main PhD supervisor via email (see contact details in Supervisors section).