Language algorithms analyze your vocabulary on Facebook and reveal your personality, while tweets within a community can accurately prict an increas risk of cardiovascular disease: thanks to big data, psychological research has chang spectacularly. Social mia are an inexhaustible source of data. Researchers are diving into this with new methods of language analysis. For example, they use social mia to study personality, mental and physical health. This saves a lot of time and provides new insights. On the way to a prictable society?
Asking questions is no longer necessary
A nearly worn-out form of research is the traditional survey. Using questionnaires, researchers tri to laura healy learning and development- sales trainer discover people’s feelings, thoughts and personalities. But asking questions is no longer necessary thanks to the ‘establish institutions’ Facebook and Twitter, where thoughts and feelings are shar en masse and spontaneously. With automat language analysis, research psychologists have already come a long way. An enrichment for psychology, they say.
Recently (late February), the highlights of the research so far were discuss at the annual conference of The Society for Personality and Social Psychology . “The new large-scale datasets are yielding studies and insights that would likely never have been possible through traditional research,” claims Hansen Andrew Schwartz of the University of Pennsylvania.
My girlfriend. My. My!
For example, consider the possessive pronoun “mine.” His research into differences in language on don’t be a water carrier social mia across personality, gender, and age shows that men use the word “mine” much more often on social mia when talking about their wives or girlfriends than women do when talking about their husbands or boyfriends. According to Schwartz, language analysis on social mia makes it possible to find unexpect connections that are often not found using caseno email list other analysis techniques.Analyzing specific
mia can provide new and detail insights into our world. Schwartz: “Data-driven techniques are usually limit to finding correlations rather than causation. Future analyses will go beyond the words themselves, to capture the less ambiguous meanings of language.”