A linguistic and behavioral assessment of a possible generic optimistic bias in individuals

What is people’s general outlook on the future? People often state that “things aren’t what they used to be”, or even “things are going to the dogs”, especially when referring to important contexts such as politics, culture, or social cohesion. Does that mean that people, by nature, in general or on average, are pessimistic?

It is well-established that individuals tend to prefer well-known things over unknown things. Through mere exposure (Zajonc, 1968), people form positive attitudes toward objects, symbols, and people that surround them daily – familiarity breeds liking. Because past events are more familiar than future events, it would make sense to grow fond of the past, and be wary of the future.

However, other mechanisms suggest that people might be positive about the future. Weinstein (1980) found that individuals generally believe to have less risk of experiencing negative events than others. Recently, Thorbjornson et al. (2015) showed that evaluations of fictitious future products were more positive than of current products. Also, construal level theory implies that people’s assessments of things that are further away in space, social distance, or time are more affect-laden and therefore possibly more positive (Trope & Liberman, 2010).

In line with the latter, we observed that sentiment scores (as provided by monitoring service Coosto) of social media messages referring to a future time (“tomorrow”) are more positive than those referring to the past (“yesterday”). This observation seems to be reoccurring over different time lags (“next/last week/month”) and time periods.

Because we believe determining whether people actually have a generic “optimistic” bias is an important stepping stone in explaining people’s behavior (e.g. stock trading, risk taking, voting, or planning) and their narratives, we need to establish whether we can validate the tentative Coosto results by (1) systematic and reliable computational linguistic analysis and (2) experimental studies.

Share