ABSTRACT
Though previous work has demonstrated transfer of sadness between CMC dyads, this study sought to better understand transfer of high-energy, negative emotion in groups. Affect was induced in one of three group members via a film clip after which participants engaged in a group activity to facilitate angry affect transfer. Emotional transfer occurs in this setting, evidenced by higher negative affect, more disagreement, higher verbosity, and use of more complex language in groups transferring angry affect compared to groups sharing no specific emotions.
Author Keywords
Computer-mediated communication, affect, group, emotion.
ACM Classification Keywords
J4: Social and behavioral systems: Psychology.
INTRODUCTION
Through emotional contagion, members of groups can influence the emotion or behavior of other members via the conscious or unconscious transfer of emotions, behaviors and attitudes [1] While emotional contagion has been examined in face-to-face (Ftf) dyads and groups, it is a little studied phenomenon in computer-mediated communication (CMC) [1]. Though early work suggested that nonverbal cues were essential for emotional expression and perception [2], recent work shows that emotional contagion occurs in CMC [3] [4].
Since ostentatious displays of emotion are discouraged in social settings, emotions must be conveyed in more subtle forms via physical cues [2]. As CMC lacks these cues (e.g., facial expressions, gestures, and vocal inflection), it is a reasonable assumption that emotional understanding would be impugned in CMC.
Additional research demonstrates the ability to convey and transfer emotions through linguistic cues. In this study, when one subject was induced to feel sad before a CMC interaction, the other subject was able to detect negative emotion in the subject, and reported feeling sadder after conversing with the partner. Sad participants used fewer words, disagreed more, used more negative-affect words, and responded more slowly time. Despite the absence of nonverbal cues in CMC, people can perceive and transfer negative emotion.
Because emotional detection in CMC relies on attention to linguistic cues, when cognitive resources are distributed among language of several individuals, will emotional detection be deterred? Our research explores this question, and determines whether contagion can occur in group CMC. Unlike those who multitask to a lower degree, heavy media multitaskers are less able to effectively switch tasks [6]. This suggests that when attention is dispersed, the ability to filter out irrelevant interference is weakened.
To address these issues, our study employs high-energy, negative emotion in the form of anger. Though previous contagion studies in Ftf contexts did not demonstrate differences in the degree of emotional contagion based on emotional energy, given the lack of physical cues in CMC, we predict that energy level will be an important factor in emotional contagion in CMC due to the higher visibility of high-energy emotion in text. We thus propose the following hypotheses:
H1: Groups sharing high-energy, negative emotion in CMC will report feeling more negative emotion, compared with group members sharing no specific emotions.
Previous research also suggests that linguistic differences should emerge between groups sharing emotions and emotionally neutral groups, thus we propose the following hypotheses:
H2: Groups sharing high-energy, negative emotion will use more negative affect words compared to groups sharing no specific emotions.
H3: Groups sharing high-energy, negative emotion will disagree more often compared to members in groups sharing no specific emotions.
Though previous research showed that inducing sadness led to participants using fewer words and less punctuation in CMC [3], we believe that using a high-energy, negative emotion (e.g., anger) may lead to the use of more words and punctuation due to its arousing nature. Thus:
H4: Groups sharing high-energy, negative emotion will use more words than groups sharing no specific emotion.
H5: Groups sharing high-energy, negative emotion will use more punctuation than groups sharing no specific emotion.
Methods
Participants
The participants (N=84) were students who either received class credit or $10 for their participation. There were 25 male and 47 female participants (ranging in age from 18 to 26 years old); two participants did not provide their gender. Participants signed up in groups of three friends with 27 same-sex and mixed gender groups participating in the study. Sixteen groups were randomly assigned to the angry affect group and twelve groups were randomly assigned to the neutral affect group.
Procedure
Phase I: When the participants arrived, groups were randomly assigned to either the angry or neutral affect condition. All participants were randomly assigned to one of three rooms and were told that they would be participating in two unrelated studies, the first being a “perceptions of film” study. Participants then watched a 7-minute video clip. In the angry affect condition, one participant (the angry emotion experiencer) was randomly assigned to view a clip from the film My Bodyguard depicting instances of bullying and injustice, which previous research has shown to induce anger. The other two partners of the group watched a clip from Before Sunset, a boring film clip that was chosen to have no effect on affect [3]. In the neutral affect condition, all three participants watched the Before Sunset clip.
After watching the clips, all participants filled out a questionnaire that measured emotion using a PANAS scale. This emotional scale was used to ensure the success of the anger manipulation and asked participants to assess their level of feeling on a 7-point Likert scale for twenty affect words (e.g., hostile, guilty, inspired, etc.). A high-energy, negative affect factor was created to assess the effectiveness of the anger manipulation on the experiencers in the present study and was reliable (Cronbach’s ยต = .88).
Phase II: All participants were then informed that they would begin the second study, a “group multi-tasking in CMC” experiment. Their primary task was to chat with the other two members of their group via AOL Instant Messenger to generate a list of “Three Tips for Surviving Freshmen Year.” To motivate participants to talk about themselves (a highly emotional subject), every tip the group came up with was to be accompanied by an example explaining the tip’s personal relevance.
In the angry affect condition, the experiencer was asked to simultaneously listen to heavy metal music and solve word scrambles that all solved to words with an anger-related prime (e.g., limaec > malice) to maintain negative affect. In the neutral affect condition, participants listened to light jazz music and completed word scrambles that solved to neutral primes (e.g., ldbna > bland) to maintain neutral affect. The manipulation was presented as a study on how multiple stimuli affect group productivity. As added incentive, participants were told that the group with the best tips would be awarded $20 gift certificates.
Following the activity, all participants filled out a second questionnaire measuring their emotional state using the Circumplex Affect Scale [7]. Participants were additionally questioned about their perceptions of their group members’ performance and emotional states.
Participants were then debriefed, with particular focus on ensuring that participants in the angry affect condition did not feel anxious or angry after the clip and activity. Participants were given cookies upon exit to dispel residual negative affect.
Manipulation Check
Induction of anger via the My Bodyguard video clip was successful, with no gender effects. Experiencers who watched the anger-inducing clip reported having significantly more high-energy, negative affect on the PANAS scale (M = 2.57, SD = 1.06) than participants who watched the neutral clip (M = 1.23, SD = .28), t(80)= 9.21, p < .001.
A second manipulation check assessed experiencers’ mood after the group activity. The induction was partially maintained as participants who watched the angry clip were marginally more annoyed (M = 1.81, SD = .83) and frustrated (M = 1.81, SD = 1.05), than participants who watched the neutral clip (M = 1.38, SD = .80; M = 1.42, SD = .75), t(80) = 1.93, p = .06; t(80) = 1.72, p = .09. The reason for marginal significance after the group activity may have been due to the emotional contagion occurring between participants in the angry affect condition. An analysis comparing experiencers to the neutral affect condition participants revealed that experiencers were significantly more annoyed (M = 1.81, SD = .83) and frustrated (M = 1.81, SD = 1.05), than neutral affect participants (M = 1.29, SD = .69; M = 1.32, SD = .70, t(52) = 2.38, p < .05, t(52) = 2.04, p < .05, indicating that our manipulation was successfully maintained.
Linguistic Analysis
The language of group chats was analyzed using the Linguistic Inquiry and Word Count (LIWC) data analysis program [8]. LIWC explores word count, frequency of word use and punctuation based on a variety of different psychological dimensions. In the present study the following dimensions were explored: negative emotion words, discrepancies, word count, and punctuation.
Results and Discussion
Perception of Group Members
Initial analyses were conducted to determine whether participants perceived differences in emotionality of their partners, both within the angry affect and neutral affect conditions. These analyses revealed no significant differences within or between groups in perceived emotion of group members, suggesting that none of the participants were aware of emotional differences, despite the success of the angry affect manipulation on experiencers. There were also no differences in participants’ ratings of closeness with their group members or in participants’ perceptions of group members contribution to the group’s performance on the activity.
Emotional Contagion
Our first hypothesis suggested that groups sharing negative emotion would report feeling more negative emotion, compared with group sharing no specific emotions. If this was the case, partners who interacted with experiencers in the angry affect condition should report more negative affect than participants in the neutral condition. Analysis comparing only partners to the neutral condition revealed that partners reported being significantly more tense (M = 1.64, SE = .13), than participants in the neutral condition (M = 1.19, SE = .12), F(1, 21)= 6.96, p < .05. Experiencers also felt significantly more tense (M = 1.79, SE = .19) than neutral participants (M = 1.20, SE = .19), F(1, 21)= 5.78, p < .05. Means are in Table 1. These findings in combination suggest that the same negative emotion, tension, was transferred within groups sharing angry affect, thus supporting H1. No other significant differences between partners and neutral participants were observed.
Additional analyses were conducted to determine differences between emotions felt by anger experiencers, partners and the neutral affect condition to determine whether the group activity affected experiencers, partners and neutral affect participants differently. Table 1 indicates that partners felt significantly more glad and content than experiencers. Additionally, experiencers felt more alarmed than neutral participants.
| | Angry | Neutral | |
| Emotion | Experiencer | Partner | All |
| Glad | 2.31a (.30) | 3.25b (.21) | 2.81ab (.18) |
| Content | 2.75a (.23) | 3.54b (.19) | 3.39ab (.16) |
| Alarmed | 1.63a (.30) | 1.25ab (.13) | 1.05b (.04) |
| Tense | 1.81a (.26) | 1.64a (.17) | 1.18b (.06) |
Table 1. Post-activity emotion means with standard deviations for experiencers, partners and neutral condition.
Note. Comparisons between means, specified by lowercase superscripts, are horizontal only. Cell means that do not share a letter in their superscripts differ at p < .05 according to Tukey’s HSD.
Linguistic Differences
The remaining hypotheses explored whether there would be linguistic differences between the angry affect and neutral affect conditions. Results for linguistic categories are displayed in Table 2. The second hypothesis (H2), which predicted that participants in the angry affect condition would use more negative affect words than neutral affect participants as there were no differences in the use of negative emotion words between these groups, F(2, 59) = 1.03, p = .364.
Next, we predicted that participants in the angry affect condition would disagree more than neutral affect participants (H3). There were significant differences in the amount of discrepancy words (e.g., couldn’t, should’ve, wouldn’t) used by participants, such that angry affect participants used significantly more discrepancy words than neutral participants, F(2, 59) = 6.39, p < .01, supporting H3.
The fourth hypothesis explored whether participants in the angry affect condition used more words in general than those in the neutral affect condition. This hypothesis was confirmed as partners and experiencers used significantly more words than neutral affect participants, F(2, 59) = 3.79, p < .05.
Our final hypothesis predicted that participants in the angry affect condition would use more punctuation than participants in the neutral condition. H5 was not supported as there were no differences between groups in punctuation use, F(2, 59) = .64, p = .533.
In addition to our hypothesized findings, some interesting differences emerged between participants. Patterns are displayed in Table 2.
| Category | Angry | Neutral | | |
| Experiencer | Partner | All | p | |
| Word Count | 227.09ab (28.49) | 231.10a (19.32) | 171.03b (13.98) | .03 |
| Function | 52.32a (1.81) | 51.96a (1.32) | 45.70b (1.20) | .00 |
| Verb | 16.81ab (.70) | 16.85a (.82) | 14.11b (.58) | .01 |
| Present | 11.78a (.80) | 11.71a (.74) | 9.94a (.41) | .05 |
| Quantifiers | 3.88a (.43) | 3.84a (.35) | 2.90a (.25) | .05 |
| CogMech | 19.21a (.68) | 16.87a (.65) | 14.10b (.67) | .00 |
| Discrepancy | 2.19a (.33) | 1.87a (.22) | 1.13b (.16) | .00 |
| Tentative | 3.24a (.42) | 2.66ab (.31) | 2.06b (.23) | .04 |
| Preps | 10.27a (.43) | 9.95a (.57) | 8.49a (.43) | .03 |
Table 2. Lingusitic profiles of emotion across angry and neutral groups.
Note. Comparisons between means, specified by lowercase superscripts, are horizontal only. Cell means that do not share a letter in their superscripts differ at p < .05 according to Tukey’s HSD.
Discussion
Emotional contagion in group CMC operates differently than with dyads [3]. Unlike previous studies [3] [4], participants were unable to recognize emotional differences in partners, suggesting that the process occurs via more subtle means in groups. This may be due to the noise of having to process multiple group members’ messages. Additionally, it seems that though angry affect expressed by experiencers does result in more negative affect in partners in the form of tension, partners are not experiencing anger. Though experiencers may feel more high-energy, negative emotion (e.g., frustration, annoyance) they are not expressing these frustrations outwardly to their partners, which may be socially inappropriate [2]. The finding that negative emotion words were not used more frequently by angry affect than neutral affect groups is reflective of this type of restraint on the part of experiencers. Indeed, no linguistic differences were found between partners and experiencers language patterns.
The finding that partners were significantly more content and glad than experiencers was curious and may be indicative of relief from mental exhaustion that they experienced by interacting with experiencers. Higher within group disagreement via linguistic discrepancies suggests that the interaction was more mentally taxing, which could explain these differences.
Though the finding that people disagreed significantly more between groups supports linguistic findings [3] [4], analyses in this study advance work in this area by demonstrating differential effects on word count. The induction of high-energy, negative emotion led both experiencers and partners to use significantly more words than neutral affect participants, providing further evidence of contagion of emotional energy within the angry affect group. Though early work in emotional contagion in CMC [3] [4] focused primarily on manipulating the valence of emotion in these contexts, our findings highlight the importance of considering emotional energy in understanding contagion processes in CMC specifically.
Additional linguistic findings in this study suggest that sharing high-energy, negative emotion affects complexity of language use. Participants in the angry affect condition used significantly more cognitive mechanism words and prepositions in their chats than neutral affect participants. These types of words are indicative of more complex language and are used more frequently in complex sections of texts (e.g., the discussion section of research articles) [8]. Given that the task instructions explicitly encouraged participants to share personal stories as examples for their tips, it seems that angry affect participants were more focused on completing the task based on the instructions than neutral affect participants. Though further work needs to be conducted to determine the specific relationship between emotion and performance, these findings have interesting parallels with earlier research on the relationship between stress and performance. Too much or too little stress can negatively affect performance, but people perform better at moderate stress levels [9]. Though we do not have data that speaks to actual performance per se, the differences in cognitive complexity between groups suggest that angry affect participants’ are engaging in more task-oriented behaviors, which should lead to better performance.
Conclusion
In addition to demonstrating emotional contagion in CMC contexts, the present study makes several important advances in understanding this process in CMC. First, this work demonstrates the importance of emotional energy in understanding the way that emotions affect CMC interactions.
Second, this study suggests that the process of emotional contagion in CMC is more complex than previously thought. Though participants in the angry affect condition shared tension, they differed in their experiences of more positive emotions (e.g., content, happy), suggesting that expressions of affect occur via more subtle mechanisms and beyond partners’ conscious awareness. Future work should explore whether participants are aware of their partners’ emotions on an implicit level.
Finally, this study suggests that emotional contagion may be a useful tool for encouraging more complex thought processes and deeper discussion in virtual groups. The tension felt by members of angry affect groups was actually an efficient means of encouraging task focus. Though our current data don’t speak specifically to performance, task behaviors should be explored in greater detail using emotions of different valence and energy to determine, which emotions may encourage optimal performance without causing negative impact.
REFERENCES
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