The PBSDS: A dataset for the detection of pseudoprofound bullshit

“Bullshit" refers to communication that is designed to impress but is constructed without concern for truth [1]. Bullshit differs from lying in that the liar deliberately manipulates and subverts truth (usually with the intent to deceive), while the bullshitter is simply unconcerned with what is true and what is false. A liar needs to know the truth value of a proposition; the bullshitter simply does not care. Although bullshit comes in different forms, in this project, we focused specifically on what is referred to as “pseudoprofound bullshit," which is designed to convey some sort of potentially profound meaning but is actually semantically vacuous [2], e.g., “Hidden meaning transforms unparalleled abstract beauty." Table 1 reports further examples of pseudoprofound bullshit and nonpseudoprofound bullshit sentences from our dataset. The goal of this project is to construct a dataset of tweets that contain pseudoprofound bullshit in English (the PBSDS).1 Operating under the assumption that bullshit is similar to spam email, we hypothesize that it should be possible to detect pseudoprofound bullshit using relatively simple classification algorithms.