Context

The NanoBubbles ERC Synergy project's objective
(https://nanobubbles.hypotheses.org) is to understand how, when and why
science fails to correct itself. The project focuses on claims made
within the field of nanobiology. Project members combine approaches
from the natural sciences, computer science, and the social sciences
and humanities (Science and Technology Studies) to understand how error
correction in science works and what obstacles it faces. For this
purpose, we aim to trace claims and corrections through various
channels of scientific communication (journals, social media,
advertisements, conference programs, etc.) via both qualitative and
digital methods.


Intership objectifs

Scientific articles are now discussed in a variety of mediums. The
social network Twitter is particularly favored by several
professionals, such as journalists and scientists, as a way of staying
updated about recent development in their field, publicly discussed
their work with distant colleagues and engage outside parties in their
discoveries.

Citing scientific articles on Twitter is easily done using publishers
sharing links. Studies focusing on the use of social network by
scientists (Costas 2015, 2017), the propagation of scientific
information (Mohammadi 2018, W ?uhrl 2021, Hou 2022) and how the use of
Twitter may influence back research (Ortega 2017). These studies rely
heavily on the hyperlinks present in Twitter posts or on tools
providing data on the use of research in social networks like PlumX
(Champieux 2015).

However, a scientific article citation can be present in a tweet as a
'fuzzy mention' (e.g. I have read in a paper written by AUTHOR in 20XX
that ...). These fuzzy mentions are hard to detect and need to be
linked back to the article they refers to in order to be taken into
considerations.

The intern first task will consist in collecting a corpus of tweets
containing such 'fuzzy mention' of scientific articles. Afterwards he
will apply existing extraction technics and models, mainly Named Entity
Recognition, in order to extract the information enabling to (1)
determine that a twitter post does mention an article and (2) link this
article to a bibliographic database.


Skills

-   Being enrolled in a Master in Natural Language Processing,
    computer science or data science.
-   Good programming skills in Python, including experiences with
    natural language processing tools and methods, knowledge of machine
    learning methods and deep learning models.
-   Curiosity for scientometrics.
-   Ability to communicate and write in English is a plus.


Scientific environment

The work will be conducted within the Sigma team of the LIG laboratory
(http://sigma.imag.fr). The recruited person will be welcomed within
the team which offer a stimulating, multinational and pleasant working
environment.


Instructions for applying

Applications must contain a CV + letter/message of motivation + master
grades + letter(s) of recommendation (or names for potential letters),
and be addressed to Cyril Labbé (cyril.labbe@imag.fr) and Martin
Lentschat (martin.lentschat@univ-grenoble-alpes.fr). Applications will
be considered on the fly. It is therefore advisable to apply as soon as
possible.


References

-   Champieux, R. (2015). PlumX. Journal of the Medical Library
    Association: JMLA, 103(1), 63.
-   Costas, R., Mongeon, P., Ferreira, M. R., van Honk, J., &
    Franssen, T. (2020). Large-scale identification and
    characterization of scholars on Twitter. Quantitative Science
    Studies, 1(2), 771-791.
-   Costas, R., van Honk, J., & Franssen, T. (2017). Scholars on
    Twitter: who and how many are they?. arXiv preprint
    arXiv:1712.05667.
-   Mohammadi, E., Thelwall, M., Kwasny, M., & Holmes, K. L. (2018).
    Academic information on Twitter: A user survey. PloS one, 13(5),
    e0197265.
-   Hou, J., Wang, Y., Zhang, Y., & Wang, D. (2022). How do scholars
    and non-scholars participate in dataset dissemination on Twitter.
    Journal of Informetrics, 16(1), 101223.
-   Wührl, A., & Klinger, R. (2021). Claim detection in biomedical
    Twitter posts. arXiv preprint arXiv:2104.11639.
-   Ortega, J. L. (2017). The presence of academic journals on
    Twitter and its relationship with dissemination (tweets) and
    research impact (citations). Aslib journal of information
    management, 69(6), 674-687.