Background For my master thesis in Public Administration, I'm working on a model to estimate the effect of knowledge claims on retweet count. I'm looking at a data set of 500,000 tweets of EU Parliamentarians. Via a keyword search of words like 'data', 'evidence', or 'statistic', I'm identifying which tweets contain references to knowledge or evidence. I assume that these references are deployed by the political actors to validate their claims and generate greater diffusion of their messages on Twitter.

Problem I've used a multiple linear regression to estimate the effect of the dummy variable 'knowledge claim' (1 for present, 0 for missing) on retweet count, while applying control variables such as follower count and status count. As the results are not very stable across different models, I tried to understand my data better and found that retweets follow a power-law distribution. I believe this is why a linear regression model doesn't produce the best results.

I'm reaching out here to ask if you have a suggestion for me how I can approach this problem. I think if I had a continuous independent variable, I could apply the natural log to both variables to fit the model better, but since my independent variable is dichotomous, I'm not sure if this is the best approach. Should I only apply the log to retweet count? Or do you have any other idea? I'm happy about any ideas or literature suggestions.

I hope this question is not too broad to ask it here.

Thank you!


Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Browse other questions tagged or ask your own question.