In 1993, a famous New Yorker cartoon by Peter Steiger coined the famous axiom representing the difficulty of understanding other people online: “On the internet, nobody knows you’re a dog.” Although technological advancements have greatly changed online interactions in the
In 1993, a famous New Yorker cartoon by Peter Steiger coined the famous axiom representing the difficulty of understanding other people online: “On the internet, nobody knows you’re a dog.” Although technological advancements have greatly changed online interactions in the subsequent years, the internet continues to have a number of limitations as a platform for interaction. Compared to face-to-face interaction, successful online communication requires additional attention and motivation. Social cues can be ambiguous or absent, and the variety and fleeting nature of the information can be overwhelming. In addition, users have a large degree of control over the information and people they encounter, meaning there can be minimal social consequences to misunderstandings online.
Because of this combination of an increased ability to filter content along with an overwhelming amount of ambiguous information, many researchers have expressed concern over the possibility that internet users selectively filter out or ignore material and perspectives they dislike, disagree with, or do not quickly understand (e.g., Reference Bakshy, Messing and AdamicBakshy, Messing & Adamic, 2015; Reference Barberá, Jost, Nagler, Tucker and BonneauBarbera, et al., 2015; Bennett & Iyengar, 2008). In other words, social media use may be particularly prone to promoting closed-mindedness.
One characteristic that might address this problem is Actively Open-Minded Thinking (AOT; Reference BaronBaron, 1993, in press), the dispositional willingness to seek out and thoughtfully engage with new and even threatening information. In this paper, we demonstrate explicit benefits of trait AOT in the accurate perception of other people online; then, using data-driven methods such as Natural Language Processing, we present a picture of how AOT manifests itself in online behaviors.
1.1 Challenges in computer-mediated communication (CMC)
Computer-mediated communication (CMC) using text is an inherently limited channel for interaction when compared to face-to-face interactions: it cannot convey important cues such as facial expression, tone of voice, and body language (Reference Sproull and KieslerSproull & Kiesler, 1986). Some researchers believe that the absence of these cues presents difficulties that cannot be easily overcome, leading to miscommunication (Reference Kruger, Epley, Parker and NgKruger, Epley, Parker & Ng, 2005) and misperceptions of others (Epley & Kruger, 2004; Reference Okdie, Guadagno, Bernieri, Geers and Mclarney-VesotskiOkdie, Guadagno, Bernieri, Geers, Mclarney-Vesotski, 2011). Some evidence suggests that such misperceptions even extend to assessments of a person’s membership in basic demographic categories such as age and gender, which are typically automatic and easy in face-to-face communication (Reference Quinn, Yahr, Kuhn, Slater and PascalisQuinn et al., 2002; Reference Tranel