The question we ask is whether online echo-chambers on social media networks enhance the anxiety and depression of individuals during the COVID19 outbreak. More specifically we want to measure the intensity of the communication about COVID-19 within the echo-chamber of individuals on Twitter and investigate the impact on their subsequent tweets in terms of the level of anxiety and signs of depressive language in their Tweets. We measure echo-chambers by the number of users in the social network that tweeted about COVID-19. We build on an extensive dataset of Twitter users for whom we have identified a large number of demographic and geographic variables (such as the gender, age, ethnicity, location by state, political affiliation) as well as their social network.

Outcome

  • Tweet collection
  • Content Analysis of Tweets (Anxiety and Depression detection)
  • Calculation of Echo Chamber Scores
  • Modelling of the impact of echo-chambers on subsequent anxiety and depression

Learning opportunity

Being a project with social scientists we believe this project would give students experiences and opportunity for expertise that goes beyond typical data science skills.

One selected candidate may receive a stipend via the DSI Scholars program. Amount is subject to available funding.

Faculty Advisor

  • Professor: Oded Netzer
  • Department/School: Columbia Business School/Marketing Department
  • Location: Morningside

Project Timeline

  • Anticipated workload: 15-20 hrs. for 4 weeks
  • Duration: The project should be completed in a month. The collaborators will provide the existing data and offer support in the further data collection and analyses.

Candidate requirements

  • Skills required:
    • Ability to access unstructured (textual data)
    • NLP and other text mining skills.
    • Running statistical analyses (e.g., regression, logit, random effect models in R or Python)
  • Student eligibility: freshman, sophomore, junior, senior, master’s