Social Responses to Media Technologies in the 21st Century: The Media are Social Actors Paradigm

Paper · Source
Design Frameworks

we propose the Media are Social Actors (MASA) paradigm as a structured extension of the CASA paradigm. We suggest that an enhanced framework that builds on the CASA paradigm, expounds the effects of social cues, describes the psychological mechanism of social responses, and provides propositions that scholars can test will not only direct future research on human-technology interaction but also meet the criteria of explanatory power, predictive power, falsifiability, heuristic value, and internal consistency that Chaffee and Berger (1987) outlined for a rigorous theoretical framework.

A large body of research suggests that technologies with more social cues can evoke stronger social responses than ones with fewer social cues

that (1) the quality of social cues should receive more attention in future research, (2) the roles of individual differences and communication contexts should be considered in the paradigm, (3) the two prominent psychological explanations for social responses, mindlessness and anthropomorphism, can be unified and applied to explain a wide variety of human-machine communication (HMC) scenarios, and (4) specific propositions should be developed and refined to derive future research questions on HMC.

Examples of social cues include a social actor’s voice, humanlike appearance, and eye gaze, whereas social signals include perceivers’ translation of these and other cues into an understanding of the social actor’s emotion (e.g., indicated by the actor’s smiling face), attention (e.g., indicated by eye contact), empathy (e.g., indicated by hugs or language), and so on.

assigned a female voice and a male voice to the computers and found that a female computer was perceived to be more familiar with love and relationships, whereas a male computer was more knowledgeable about technical subjects.

P1: Every media technology has at least some potential to evoke medium-as-social- actor presence and corresponding social responses.

P2: It is not only the social cues but also the combination of social cues, social signals, individual factors, and contextual factors that lead to medium-as-social- actor presence and corresponding social responses.

P3: Some social cues are primary: Each is sufficient but not necessary to evoke medium-as-social-actor presence.

P4: Some social cues are secondary: Each is neither sufficient nor necessary to evoke medium-as-social-actor presence.

P5a: All other conditions being equal, individuals are more likely to experience medium-as-social-actor presence and socially respond to media technologies that display cues with more human characteristics (quality of cues).

P5b: All other conditions being equal, individuals are more likely to experience medium-as-social-actor presence and socially respond to media technologies that display more social cues (quantity of cues). Lombard and Xu 43

P6: All other conditions being equal, the quality of cues (primary vs. secondary) has a greater role in evoking medium-as-social-actor presence and corresponding social responses than the quantity (number) of cues.

P7: Individuals vary in their tendency to perceive and respond to media technologies as social actors.

P8: Individuals’ social responses to media technologies can occur with either mindless or mindful processing.

P9a: All other conditions being equal, media technologies that display more cues (quantity of cues) are more likely to lead individuals

Considering that Kim and Sundar (2012) noted that anthropomorphism can occur mindlessly or mindfully, we use mindless anthropomorphism and mindful anthropomorphism to unify the explanations for medium-as-social-actor presence and social responses

Lines of inquiry this paper opens 24

Research framings built by reading the notes related to this paper — the questions it feeds into.

What prevents conversational agents from taking initiative in dialogue? What mechanisms preserve shared understanding in evolving conversations? How well do AI systems understand human social norms? What drives appropriate trust calibration in personalized AI systems? What design and behavioral factors drive false consciousness attribution to AI? Can AI systems distinguish genuine empathy from simulated emotion? How do neighboring agents influence whether others cooperate or collude? What determines appropriate intervention timing and manner for AI agents? Should AI communication design follow human conversation norms or develop distinct machine-specific principles? How can AI chatbots provide therapeutic benefit without causing harm? What safeguards enable trustworthy AI-assisted scientific peer review at scale? What enables genuine semantic understanding in language models? How does AI-generated content undermine authentic engagement on social platforms? How does dialogue structure affect linguistic grounding and shared meaning? Why does polished presentation create unearned authority in AI outputs? Do language models lack essential therapeutic presence and engagement?