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E MOTION, SOCIAL INFLUENCE, & THE MASS MEDIA Robin Nabi University of California, Santa Barbara December 2007.

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Presentation on theme: "E MOTION, SOCIAL INFLUENCE, & THE MASS MEDIA Robin Nabi University of California, Santa Barbara December 2007."— Presentation transcript:

1 E MOTION, SOCIAL INFLUENCE, & THE MASS MEDIA Robin Nabi University of California, Santa Barbara December 2007

2 BROAD RESEARCH INTERESTS  SOCIAL INFLUENCE  INTERPLAY BETWEEN EMOTION & COGNITION  MASS MEDIA EFFECTS (in health context)

3 PRESENTATION OVERVIEW  Brief introduction to discrete emotions and perspectives on emotion/media  Conceptual Frameworks  STUDIES 1-3: Coping with regret  Future research directions

4 FUNCTIONAL THEORIES OF EMOTION  (e.g., Lazarus’, 1991, Cognitive- Motivational-Relational theory) Emotions serve adaptive functions Emotions represent person- environment relationships associated with:  goals  goal-consistent action

5 EMOTION THEMES & MOTIVATIONS TO ACT

6 DISCRETE EMOTIONS, INFLUENCE, & MEDIA  Fear appeal/persuasion research  Children & fright reactions to entertainment media  Emotional news  And??? Dimensional approaches Empathy, suspense, horror research Mood Management Theory (MMT)

7 LIMITS  MOSTLY FOCUSED ON ONE EMOTION - FEAR  COMPARTMENTALIZES MEDIA CONTEXTS  FOCUS ON MEDIA AS CAUSE OF EMOTION

8 DEVELOPING CONCEPTUAL FRAMEWORKS  COGNITIVE-FUNCTIONAL MODEL (Nabi, 1999, 2002) Influence of a range of negative emotions on information processing and persuasion KEY: nature of emotion x message expectation  EMOTIONS-AS-FRAMES MODEL (Nabi, 2003, 2007) More general media application

9 EMOTIONS-AS-FRAMES

10 CoDE MODEL  Coping with Discrete Emotions via media use (Nabi, in progress)  Assumes media exposure predicted by: Action tendency associated with emotion Subjective likelihood of programming meeting emotion-induced need

11 CoDE MODEL

12 CoDE MODEL vs. MOOD MANAGEMENT THEORY  Different assumptions about driving force behind media selection  CoDE Selection/perception based on need to reframe person-environment situation  MMT (Zillmann, 1988, 2000): Media selection driven by hedonistic motives to regulate arousal/mood

13 MMT & EMOTION Q: WHAT DOES MMT SAY ABOUT HOW SPECIFIC EMOTIONS AFFECT MEDIA SELECTION?  Very little 

14 MMT, EMOTION, & SELECTION 4 POSSIBILITIES 1. Avoid messages with similar content (semantic affinity) 2. Avoid all media (specific emotions like anger) 3. Watch information/ educational programming (to aid informational needs/coping) ALL ELSE FAILS…. 4. Watch entertainment programming to meet informational needs (catch-all for counterhedonistic choice)

15 STUDY 1: DOES MISERY LOVE COMPANY? (Nabi et al., 2006)  Predicting entertainment program selection based on functional emotion perspective  Topic: regret over cheating on a romantic partner  “Competing” hypotheses: MMT v. Functional Theory (Regret Theory)

16 REGRET THEORY REGRET: the painful sensation of recognizing that ‘what is’ compares unfavorably with ‘what might have been’ (Landman, 1991) ADAPTIVE FUNCTION avoid repeating irrevocable mistakes KEY APPRAISALS 1. COUNTERFACTUAL THINKING (assign value to different outcomes) 2. SELF-BLAME

17 REGRET THEORY & MEDIA SELECTION  Most regretted experiences are personal/life decisions  Entertainment programming reflect personal/life decisions THUS EP  insight into regretted experience  change value of chosen option/reduce self-blame  reduce regret

18 METHOD  144 Undergrads  54% female; 46% male  53% had cheated on a romantic partner  ASSESSED PAST CHEATING BEHAVIOR  FEELINGS/ANTICIPATED FEELINGS ABOUT CHEATING 4 items (regret, remorse, guilt, shame;  =.91)  PERSONALITY TRAIT FILLER SCALES

19 METHOD: SURVEY DV  INTEREST IN 9 STORYLINES 3 related to cheating 6 unrelated to cheating

20 METHOD: EXPERIMENT  WATCHED 1 OF 2 VERSIONS OF 7-minute STORYLINE FROM REAL WORLD TWO DIFFERENT ENDINGS  WOMAN CHEATS  EXPRESSES REGRET  WOMAN CHEATS  RATIONALIZES HER BEHAVIOR

21 METHOD: EXPERIMENT  HOW MUCH ENJOYED SEGMENT (4 items;  =.91)  HOW MUCH REGRET PAST CHEATING  CONTROL VARIABLES Perceived realism, homophily, seen the program/story line

22 MANIPULATION CHECKS  Cheaters identified with protagonist more than non-cheaters M=3.32 v. M=2.00, p <.001  Those cheated on identified more with protagonist’s boyfriend M=4.83 v. M=3.10 p <.001

23 MANIPULATION CHECK  Protagonist seen as more regretful in the self-blame condition M=4.33 v. M=2.13, p <.001

24 H1: REGRET & VIEWING INTEREST IF have cheated:  H1a: will be more likely to be interested in viewing cheating-related TV storylines if still feel regret vs. not (CoDE prediction) IF have NOT cheated:  H1b: will be less likely to be interested in viewing cheating-related TV storylines if anticipate regret vs. not (MMT prediction)

25 H1: STORYLINE INTEREST  Significant interaction b/w past cheating and regret (p <.01) Regretful cheaters: more interested in viewing Anticipating regret non-cheaters: less interested in viewing

26 H2: REGRET & ENJOYMENT IF have cheated:  H2a: will enjoy watching cheating-related TV storyline more if still feel regret vs. not IF have NOT cheated:  H2b: will enjoy watching cheating-related TV storyline less if anticipate regret vs. not

27 H2: PROGRAM ENJOYMENT  Significant interaction b/w past cheating & regret (p <.05) No diff. b/w regretful and non-regretful cheaters Non-cheaters anticipating regret less likely to enjoy program

28 H3: PROGRAM CONTENT & COPING WITH REGRET  H3: Cheaters with lingering regret will: (a) enjoy watching a media character who demonstrates rationalization more so than self-blame (no difference for low regret cheaters) (b) feel less regret about their past behavior – especially if view the rationalization ending

29 H3a: REGRET & ENDING ENJOYMENT  Significant interaction between story ending and regret level p <.05 Regretful cheaters enjoyed rationalization ending more than regret ending No diff for non-regretful cheaters

30 H3b: COPING WITH REGRET  Regret overall was reduced (M=5.63 v M=3.98, p <.001)  NOT dependent on story ending

31 STUDY 2: MISERY II – Generalized Regrets  206 Tucson residents  List 3 biggest mistakes in life  How interested in seeing: media character make same choice media character make difference choice  Trait-Regret

32 RESULTS  Those higher in trait regret: Expressed interest in watching program in which character makes same choice (r =.20**) AND different choice (r =.34***) as they made Preferred to watch different choice more than same choice (r =.15*)

33 CONCLUSION  Discrete Emotion Theory better suited than MMT to predict media choices when viewer is in a particular emotional state  Entertainment programming can help viewers cope with regret, perhaps through reframing the experience

34 LINGERING QUESTIONS….  WHY is regret reduced?  HOW long lasting are the effects?  WHAT other emotions might this apply to?

35 STUDY 3: REGRET, SEXUAL BEHAVIOR, & MEDIA  n = 121 women who had previously had a one-night stand varying degrees of regret  Viewed 1 of 6 versions of Sex & The City episodes edited to show character display: regret vs. no regret learned lesson vs. accepted mistake negative vs. positive outcome

36 STUDY 3 HYPOTHESES  H1: Viewing media depiction will reduce regret  RQ1: What re-framing mechanism explains regret reduction? Learned from mistake (adaptive function) Forgive self (reduce self-blame) Minimize behavior (change value) Normalize behavior (not alone)  RQ2: Do different depictions matter?

37 RESULTS – H1  Viewing programming reduced regret (M = 4.50 v. M = 3.97, p <.001)

38 RESULTS – RQ1 (MECHANISM OF CHANGE)  CHANGE IN REGRET CORRELATED WITH Self-forgiveness, r =.19* Minimize/rationalizing behavior, r =.19*  CHANGE IN REGRET DID NOT CORRELATE WITH: Perception not alone, r =.06 Learning from mistakes, r = -.01

39 RESULTS – RQ2 (ROLE OF DEPICTIONS)  Generally speaking, depiction didn’t matter EXCEPT: When character displayed regret (vs. no regret), rationalization of behavior increased (r =.34**)  Nature of Depiction did not interact with initial degree of Regret

40 CONCLUSIONS  Data consistent with CoDE Model in: Interest in programming (Studies 1 & 2) Reducing negative affect (Studies 1 & 3) Little evidence that nature of media depiction matters, BUT (Study 3)  behavior minimization (i.e., changing value of behavior) after seeing another’s regret may matter

41 FUTURE DIRECTIONS – CoDE Model  Continue to flesh out the model  Test additional emotions e.g., jealousy, shame  Continue to explore nature of depictions  Look at emotional coping effects over time

42 FUTURE EMOTION/MEDIA STUDIES  Positive Emotions & Persuasive Messages Q: What is the role of hope, humor, pride in promoting healthy behaviors?  Emotions and Health News Q: How do emotionally evocative news stories influence information processing, risk perceptions, and behavior?  Emotions and Internet/Health Information Seeking Q: What messages do people self-construct and to what cognitive and affective effects?  Emotions and Entertainment Media Q: What impact does emotional presentation of health issues have on public perceptions/ behaviors?


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