Generating Philosophical Statements using Interpolated Markov Models and Dynamic Templates
Table 1: Analysing 8582 TorfsBot tweets from September 2016 to April 2019
['[ITALIC]  [BOLD] Algorithm', '[ITALIC]  [BOLD] Tweets', '[ITALIC]  [BOLD] Avg. Interactions']
This bot uses both the interpolated Markov model and dynamic templates to generate tweets, and uses the interpolated Markov model as text generator for the reply algorithm. For the interpolated Markov model algorithm, the bot uses both tweets and columns written by Torfs as training data, putting more emphasis on the former by multiplying the weight of the n-grams from this source by ten. For the dynamic templates, we used the tweets as base texts and the columns for content texts. We also compare a subset of the replies, namely those not from a conversation with one single outlier user, who is responsible for 1883 total replies. From these results, we can see that the locally coherent algorithm (namely the interpolated Markov model) usually produces more prefered content than the globally coherent algorithm (the dynamic template algorithm). One reason for this result could be that, compared to spotting errors in complicated grammatical structures, it is easier to detect words that can not follow each other, which then drastically lowers the credence in the wisdom of the philosophical statement.