PhD ads analysis H/F

Updated: over 2 years ago
Location: Tremblay en France, LE DE FRANCE
Job Type: FullTime
Deadline: 16 Aug 2021

The PhD will be part of a bigger H2020 project named Trust aWare.

The student will be part of LIG and will work under the supervision of Oana Goga.

FeedingBias aims to provide data and analytical tools for a better understanding of incidental exposure to news on social media and the engagement of individuals with news. Our objective is a) to understand the influence of users' socio-demographic characteristics, their political orientation and their level of algorithmic awareness on their exposure to news, and b) to measure the contribution of these exposure and engagement biases on the polarization of the modern public sphere. FeedingBias will be based on recruiting a large sample of social media users responding to a survey and providing access to their accounts on Twitter and Facebook. All data collected within the framework of FeedingBias will be processed in accordance with the requirements of GDPR and the research design will be evaluated by the Ethics Committee of University Grenoble Alpes.

The PhD student will work under the supervision of Oana Goga and Gilles Bastin will help as co-supervisor. The goal of the PhD student will be to investigate the global effects of incidental information exposure by focusing on two research directions that together make an important and coherent thesis subject:
1. Investigate how users process the information they get exposed to. The student will measure to what extent people translate their targeted or incidental information exposure into actual news consumption. The student will be able to investigate different aspects: is there a fixed capacity to dedicate to news reading per user and what is the effect of targeted information on it; do people spend an equal amount of time for reading targeted information and incidental and selected information; what fraction of news consumption comes from targeted information; how incidental news exposure translates into beliefs.
2. Investigate biases in information exposure, their causes and impact. The student will check whether we see evidence of systemic biases in the incidental information quality across different groups of people and check if information exposure on social media platforms is influenced by the users' socio-demographic characteristics. The student will also quantify to which extent we can attribute biases in news exposure to a user's social network vs to the AI-based algorithmic curation employed by social media platforms.
For answering these questions the student will setup the measurement framework that is able to collect data online and categorize news sources and items. The student will be able to build on top of software already developed in the team and get advice from senior PhDs and research engineers. The postdoc and the permanent members will work on recruiting participants and deploying the surveys. The student will lead these two task, but he will be able to participate in the other two tasks (see 3 and 4 in the description) that will be lead by the post-doc.
During the PhD the student will be able to acquire a wide range of skills: coding and building platforms, collect data, experimental and systems design, data management, data analysis, apply machine learning and natural language processing methods to the data collected. This work will also make the student aware of the necessity of considering different aspects (ethical, societal) when using machine learning algorithms. Being at the intersection of several communities will be a plus for the student's intellectual development and prepare him for today's word and increasing need of inter-disciplinary research collaborations.
Throughout the PhD, the student will be in charge of the results dissemination by submitting papers to top conferences. We anticipate that the PhD will produce contributions to security, privacy, transparency and HCI top venues such as The Web Conference, CSCW, ICWSM, S&P, NDSS, CCS, IMC, FaccT.
Our goal is to provide the student with an excellent scientific environment and raise him to be a technically very strong researcher with high awareness of social aspects in a field that will with no doubt be in need of such researchers.



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