Keng-Chi Chang

Keng-Chi Chang

張耕齊

Hello. I am a MS & PhD student at UC San Diego.

My research interests lie in the realm of computational social science, the political economy of information technology, and political methodology. I use statistical and computational models to study large-scale human behaviors in online communities. Substantively, I am interested in how authoritarian regimes utilize informational tools for control and influence.

My recent works have been published in the Proceedings of the National Academy of Sciences, PLOS One, Journal of Communication, and Journal of Quantitative Description: Digital Media.

Here are my resume and vita. Other than my research, you will also find an updated list of useful resources for empirical work, coupled with some personal thoughts. Feel free to reach me at my email.

Interests

  • Computational Social Science
  • Political Methodology
  • Political Economy of Information

Education

  • PhD Candidate in Political Science

    University of California, San Diego

  • MS Student in Computer Science

    University of California, San Diego

  • BA in Economics

    National Taiwan University

Research

Mapping Visual Themes among Authentic and Coordinated Memes

Mapping Visual Themes among Authentic and Coordinated Memes

  • Collect coordinated IRA memes from Twitter and authentic memes from Reddit.
  • Cluster memes to find visual themes using self-supervised transfer learning.
  • Coordinated and authentic memes share visual themes but with different emphasis.
COVID-19 Increased Censorship Circumvention And Access To Sensitive Topics In China

COVID-19 Increased Censorship Circumvention And Access To Sensitive Topics In China

  • Use geolocated Tweets from China during the COVID-19 crisis.
  • Show that crisis motivates citizens to seek out sensitive information.
  • Gateway to both current and historically sensitive content is not found in countries without extensive online censorship.
Using Facebook Data to Predict the 2016 U.S. Presidential Election

Using Facebook Data to Predict the 2016 U.S. Presidential Election

  • Use 19 billion likes to measure dynamic ideological positions of users and fan pages.
  • Guess user’s geolocation by likes and measure state level support rates for candidates.
  • Assume that users would support candidates with closer ideology.
  • FB support rates predict election outcome well, and share similar trends with polls.
  • Polls systematically overestimate Clinton’s support in right-leaning states.
The Effect of Streaming Chat on Perceptions of Debates

The Effect of Streaming Chat on Perceptions of Debates

  • Asks whether social chats on livestreams affect debate viewing.
  • Assign subjects to watch debates on ABC, Facebook Live, and 538.
  • Democratic subjects assigned to the Facebook chat condition reported lower affect towards Democrats and a worse viewing experience.
  • The tone of candidate-directed comments also matter.
Connective Effervescence and Streaming Chat During Political Debates

Connective Effervescence and Streaming Chat During Political Debates

  • Collect streaming chats of US Presidential debates on Facebook Live.
  • Describe their dynamics, degrees of toxicity, and insults.
Social Mobility in Ming China: Evidence from Twelve Thousand Chin-shih Data

Social Mobility in Ming China: Evidence from Twelve Thousand Chin-shih Data

  • Challenge the conventional wisdom that civil service exam was meritocratic.
  • Use detailed data of past 3 generations to estimate the effect of family background on tests.
  • Multi-staged examination structure provides chances to partially control for ability.