Two postdoctoral positions in Exposure to Dissimilar Views in the Media University of Amsterdam, aculty of Social and Behavioral Sciences – Amsterdam School of Communication Research Netherlands

Two postdoctoral positions in Exposure to Dissimilar Views in the Media

 

Faculty of Social and Behavioral Sciences – Amsterdam School of Communication Research

 

Publication date
23 April 2018
Level of education
PhD
Salary indication
€3,475 to €4,757 gross per month, based on 38 hours per week
Closing date
1 June 2018
Hours
30 to 34 hours per week
Vacancy number
18-211

The Amsterdam School of Communication Research ASCoR, the research institute in Communication Science at the University of Amsterdam, is the largest research institute of its kind in Europe and is among the largest worldwide. More than 50 senior researchers are permanently associated with ASCoR, and its English-language PhD program has more than 35 students.

 

We currently have a Postdoc vacancy as part of the ERC Starting Grant entitled 'Citizens exposed to dissimilar views in the media: investigating backfire effects' directed by Dr Magdalena Wojcieszak. The vacancy will be linked to the Personalized Communication Project and the Digital Communication Methods Lab, part of the RPA project Communication, directed by Prof. Claes de Vreese.

 

Project description

 

In the current polarized climate, understanding between those who hold different opinions is needed more than ever. In this context, exposure to dissimilar content in the media is crucial because encountering views that challenge one’s beliefs is hoped to foster tolerance. More and more scholars are interested in media diversity and more and more policymakers encourage citizens to see dissimilar views in the media. However, exposure to difference can also do harm, increasing polarization and conflict among citizens with different opinions.

 

Despite these dangers, we lack a comprehensive model that explains when and why exposure to dissimilar views amplifies or attenuates hostilities. What encourages people to see dissimilar political content, on which issues, and in which media? Under what conditions, for whom, and why does exposure to dissimilar views lead to polarization? What can be done to minimize biased information processing and polarization, and also to maximize the benefits of encountering dissimilar political views? This project addresses these questions.

 

In this project, we investigate (1) individual, social, and system factors that together drive exposure to dissimilar political views in mass media and – particularly – in the online environment, and (2) the effects of exposure to dissimilar political views on various indicators of polarization, while (3) accounting for various political issues and intended and incidental exposure online.

 

Some of the key themes included in this project are:

  • Under what conditions does exposure to dissimilar political views occur?

    • What factors – including individual-level characteristics, self-reported interpersonal and online political discussion, and media and political system – encourage people to see dissimilar content offline and online?
    • Do these factors vary depending on a political issue in question?
  • Under what conditions does exposure to dissimilar political views backfire?

    • What are the immediate and overtime effects of exposure to dissimilar political views?
    • What are the individual-level, meso-level, and system-level factors that enhance or minimize polarization?
    • For whom, when, and why does dissimilar exposure lead to polarization and for whom it generates understanding toward citizens with different views?

 

Methodologically, this project combines three methods and advances the use of behavioral data in communication science:

  • Panel Surveys – we will conduct panel surveys on systematic samples in three countries (the Netherlands, Poland, and the United States).

  • Online behavior tracking - concurrently, we will track the actual online exposure among the same participants with the aim of identifying the specific political content that each participant sees on determined political issues.

  • Automated Content Analysis – we will use existing software and/or supervised machine learning to assess each article’s tone, positive or negative toward each issue analyzed. When combined with information on individual attitudes, the content data will tell us whether the content is like-minded or dissimilar with regard to each individual’s attitudes.

 

Requirements

 

For both positions, you should:

  • have research interest in the topic of the project, and knowledge of new technologies, platforms and capabilities;

  • have a background in communication science, political science, and/or computer science;

  • have proven expertise in quantitative research methods;

  • have experience with Computational Social Sciences and/or Data Science, including proven knowledge of Python and R for data collection and analysis;

  • have expertise in designing, executing and analyzing complex research designs that combine self-reports (experiments/surveys) with digital trace data (e.g., tracking data);

  • have knowledge of - and proven experience with - collecting and analyzing online tracking data and online content;

  • have the willingness and commitment to work in a small, multi-disciplinary team;

  • have an excellent written and spoken command of English, as demonstrated by publications in English-language journals;

  • have excellent organizational, communicative, and presentational skills;

  • hold a relevant PhD degree in behavioral or social sciences or in disciplines relevant to the project. Candidates who are due to submit their dissertation manuscript in Spring 2018 are welcome to apply.

 

Preferred starting dates are Fall 2018 (position 1, 0.90FTE) and Winter/Spring 2019 (position 2, 0.80 FTE). The possibility of complementing the positions with 0.1 and 0.3 FTE by teaching or working at other projects within the Department of Communication Science is negotiable.

 

You will be part of the Amsterdam School of Communication Research, University of Amsterdam (and of the AmDigital Communication Methods Lab and the Personalized Communications Project), and lead the development of innovative infrastructure, tools and methods that can advance Communication Science research about exposure to information in online settings.

 

Further information

You may address questions about the application procedure and other general questions to:

  • ASCoR Secretariat

Questions about the project content you may address to:

  • Dr Magdalena Wojcieszak

 

Appointment 

Position 1 preferably starts in the Fall 2018 and position 2 in the Winter/Spring 2019. Both concern, in principle, a three-year period after an initial test period. The final implementation positions is pending contractual approval.

 

Your monthly gross salary will range, depending on your knowledge and experience, between €3,475 and €4,757 (Scale 11) based on a full-time basis. 

 

We offer a pension scheme, a holiday allowance of 8% per year, and flexible employment conditions. Conditions are based on Collective Labour Agreement for Dutch Universities

 

Job application

The UvA is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. We value a spirit of enquiry and endurance, provide the space to keep asking questions and cherish a diverse atmosphere of curiosity and creativity.

 

The deadline for applications is 1 June 2018. Please state vacancy number 18-211 in the subject line of your application, and indicate whether you are applying for position 1 or 2.

 

To apply for this position, please send at least the following documents in English by email to ascor-secr-fmg@uva.nl:

  • motivation letter, including a brief discussion about how you fit with the topic and objectives of the ERC project lab;

  • curriculum vitae, including publication list and overview of academic activities and achievements thus far (e.g., conference visits, courses taken, awards);

  • proof of your PhD degree. If you have not completed your degree at the time of application, please provide a statement from your supervisor confirming the expected date of completion of your degree.

  • an academic writing sample in English (e.g., journal article, PhD dissertation).

  • a list of names that could be consulted by the RPA as a reference for the candidate (no reference letters required).

 

Please send all your documents in PDF or doc(x) format. We will only consider complete applications. #LI-DNP

No agencies please


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