Impression formation (Psychology)

To elucidate temporal sequences among and between person and situation
variables, this work examines cross-measurement spill-overs between situation
experiences S (on the Situational Eight DIAMONDS characteristics) and personality
states P (on the Big Six HEXACO) in experience sampling data in two studies. Multilevel
modeling of lagged data at tn-1 and non-lagged data at tn grants the opportunity to
examine (a) the stability (P --> P, S --> S), (b) cross-sectional associations (S <--> P), and
(c) cross-lagged associations among and between situation experiences and personality
states (S --> P, P --> S).

Social media posts are used to examine what people experience in their everyday
lives. A new method is developed for assessing the situational characteristics of social
media posts based on the words used in these posts. To accomplish this, machine learning
models are built that accurately approximate the judgments of human raters. This new
method of situational assessment is applied on two of the most popular social media sites:
Twitter and Facebook. Millions of Tweets and Facebook statuses are analyzed. Temporal
patterns of situational experiences are found. Geographic and gender differences in
experience are examined. Relationships between personality and situation experience
were also assessed. Implications of these finding and future applications of this new
method of situational assessment are discussed.