Running head: FACEBOOK CONSENSUS 1 FACEBOOK CONSENSUS 6 Facebook Consensus: The Dynamics of Social Media Responses Wendy Perez Ramos Florida International University The Dynamics of Social Media Responses Moral judgment is commonly swayed by irrelevant factors, whereby people tend to arrive at the judgment(s) about different actions as being wrong if they are predisposed to fury prior to the making of moral judgment. On the contrary, the bias for positive emotions makes unacceptable actions at times appear acceptable. In the context, dilemmas that came before the prevalent one influence the permissibility of the unwarranted actions (Kundu & Cummins, 2013). The violation of rationality norms occurs when people allow social consensus to take precedence to facts (Kundu & Cummins, 2013). In like manner, accepting conformity creates room for error and confusion to spread reign a group, whereas the making of independent decisions as well as resistance to conform tends to be socially constructive (Kundu & Cummins, 2013). In this case, resistance to conformity may be considered both moral and rational, as it is commonplace for people’s behaviors to be frequently judged based on whether the persons involved relied on their moral principles or they simply complied. Conformity is, however, considered illogical if a person holds the belief that social consensus should be awarded less weight in the decision in comparison to one’s beliefs and values (Kundu & Cummins, 2013). In a nutshell, conformity can possibly be an outcome of a rational process, whereby the concerned people chose to follow their beliefs and the truth at the expense of a lie. The seeking of knowledge continuously takes place on various social media platforms, whereby the determinants of the messages obtained by an individual are the pages followed and the friends that one has. Unfortunately, the platforms are responsible for the spread of fake news, whereby some players hide their identities and post content to reinforce their positions (Perfumi et al., 2019). Notably, social norms exist on the platforms but people’s perception of the values vary for a number of reasons, which include platform type, anonymity, and the nature of relationships between friends (Perfumi et al., 2019). Moreover, conformity to social norms in the context of social platforms varies significantly from that of face to face, while social influence therein may be categorized into norms-oriented social influence and information-oriented one. Remarkably, it would be necessary to create a distinction between the two aspects. The implication is that online users who feel that they are anonymous may experience the temptation to disregard the opinions that they could be exposed to. The other implication may be the motive of the users of online platforms. Where the intention is communication at the expense of conformity to social norms, the communicators tend to disregard the norms completely, while they ma ...

1. Running head: FACEBOOK CONSENSUS 1
FACEBOOK CONSENSUS 6
Facebook Consensus: The Dynamics of Social Media Responses
Wendy Perez Ramos
Florida International University
The Dynamics of Social Media Responses
Moral judgment is commonly swayed by irrelevant factors,
whereby people tend to arrive at the judgment(s) about different
actions as being wrong if they are predisposed to fury prior to
the making of moral judgment. On the contrary, the bias for
positive emotions makes unacceptable actions at times appear
acceptable. In the context, dilemmas that came before the
prevalent one influence the permissibility of the unwarranted
actions (Kundu & Cummins, 2013). The violation of rationality
norms occurs when people allow social consensus to take
precedence to facts (Kundu & Cummins, 2013). In like manner,
accepting conformity creates room for error and confusion to
spread reign a group, whereas the making of independent
decisions as well as resistance to conform tends to be socially
constructive (Kundu & Cummins, 2013). In this case, resistance
to conformity may be considered both moral and rational, as it
is commonplace for people’s behaviors to be frequently judged
based on whether the persons involved relied on their moral

2. principles or they simply complied. Conformity is, however,
considered illogical if a person holds the belief that social
consensus should be awarded less weight in the decision in
comparison to one’s beliefs and values (Kundu & Cummins,
2013). In a nutshell, conformity can possibly be an outcome of a
rational process, whereby the concerned people chose to follow
their beliefs and the truth at the expense of a lie.
The seeking of knowledge continuously takes place on various
social media platforms, whereby the determinants of the
messages obtained by an individual are the pages followed and
the friends that one has. Unfortunately, the platforms are
responsible for the spread of fake news, whereby some players
hide their identities and post content to reinforce their positions
(Perfumi et al., 2019). Notably, social norms exist on the
platforms but people’s perception of the values vary for a
number of reasons, which include platform type, anonymity, and
the nature of relationships between friends (Perfumi et al.,
2019). Moreover, conformity to social norms in the context of
social platforms varies significantly from that of face to face,
while social influence therein may be categorized into norms-
oriented social influence and information-oriented one.
Remarkably, it would be necessary to create a distinction
between the two aspects. The implication is that online users
who feel that they are anonymous may experience the
temptation to disregard the opinions that they could be exposed
to. The other implication may be the motive of the users of
online platforms. Where the intention is communication at the
expense of conformity to social norms, the communicators tend
to disregard the norms completely, while they may consider
them in other cases (Perfumi et al., 2019).
Moral dilemmas entail the determination of whether to accept
harm in a bid to prevent bigger catastrophes, and decision-
makers who reject harm are often viewed as warm, moral,
trustworthy, and empathetic. The concepts originated in
philosophy, an example of related sub-disciplines being
utilitarian philosophy, which considers the impartial

3. maximization of the greater good. In the context, decision-
making systems focus on the action at hand against myriad
factors, which include long-term goals, adherence to moral
rules, and the application of moral grammar. Usually, people
really care about individual moral reputation and dilemma
decisions have an impact on standing (Rom & Conway, 2018).
Furthermore, past research indicates that people can be
considerably accurate when assessing how peers view them with
self and social ratings converging when the traits in question
involve public behaviors. As an illustration that people care
about their presentation to others, many persons tailor public
images to values and preferences that are perceived as being
generally acceptable. In this case, some people are forced to
conform to pressure for the rejection of harm than accepting it.
In case of an opportunity to establish warmth through social
interactions, it is commonplace for people to exhibit other
qualities such as competence.
Social influence consists of some distinct, conceivable
differences, one of which is normative social influence. The
variant describes the influence to adhere to certain expectations
that are other people cherish. The second process is
informational social influence, which is the tendency to accept
information, which is provided by people, as evidence for the
support of reality. When both cases apply to the context of a
product, the information provided should be uniform in terms of
product quality and should possess a direct impact on the
evaluation of consumers (Cohen & Golden, 1972). Interpersonal
response orientations refer to the modes in which people
commonly respond to others. Usually, people exhibit a balance
between orientations and will be flexible based on the demands
of various situations. The bottom line, however, is that an
individual will show preference to some given orientation. In a
nutshell, social influence operates in situations that do not
exhibit strong normative pressures, while no noticeable
difference exists between high and low uniformity treatment
groups.

4. Study One
In general, we predict that participants who read unanimously
supportive feedback will rate the Facebook user’s conduct as
more acceptable than participants who read unanimously
oppositional feedback, with those who read mixed feedback
falling between these extremes.
More specifically, participants in the unanimously supportive
condition will more strongly agree with supportive survey
statements (“Abigail’s behavior was understandable, “Abigail’s
behavior was reasonable”, “Abigail’s behavior was
appropriate”, “I would advise Abigail to keep silent”, and “I
would try to comfort Abigail”) and more strongly disagree with
oppositional survey statements (“Abigail’s behavior was
wrong”, “Abigail’s behavior was unethical”, “Abigail’s
behavior was immoral”, and “Abigail’s behavior was
unacceptable”) compared to participants in the unanimously
oppositional condition, with participants in the mixed condition
falling between these extremes. However, participants in both
the unanimously supportive and unanimously oppositional
conditions will strongly agree that they would give Abigail the
same advice that her friends gave her.
Methods Study One
Participants
The students are selected randomly from Florida University for
the study and the sample size is one hundred and forty for the
study. Among 140 students 44.3% were male and 52.1% were
female, total male respondent are (n=62) and female
respondents are (n=73), only five participants did not mention
their gender. Participants consist of a population of 40%
Hispanic American (n=56), Asian Americans were 6.4% (n=9),
Caucasians were 25.7% (n=36) and Native Indians were 2.1%
(n=3). While African Americans were 17.1% ( n=24) and Asian
Americans who are almost 6.4% (n=9). See Appendices 1
Materials and Procedure
Based on the procedure used to this study, students had to look
at the Facebook of a college student named Abigail. In this

5. page, they would see a profile with a complete description of
her. Also they see a demographic section which contains a long
paragraph discussing an incident in which Abigail accidentally
got an exam answer key during an exam and used it to get the
best grade in the course. She feel ashamed about it and want
some suggestions from her friends. The advices from Abigail’s
friends varies, according to the level of understanding of her
behavior. Some of her friend think that her conduct was
corrected, while others said that she should be honest and tell
her professor about her bad conduct.
Then, participants were given a series of statements in order to
see their impressions towards Abigail and her cheating behavior
as well as whether they agree with her friends advices. All the
students have agreed to participate and got their questions sheet
without noticing that each one was part of three different
conditions. This was conducted in order to see if their feedback
support the wrong behavior of Abigail, or if they are opposed
this, or if they have a mixed feedback about it.
The participant proceeded to the second part of the study, which
was made out of a series of questions, in order to rate their
impressions towards Abigail’s behavior. They are asked to
agree or disagreed with seven about Abigail, using a scale from
1 (strongly disagree) to 6 (strongly agree). These include ,
“Abigail’s behavior was wrong”, Abigail’s behavior was
understandable”, “Abigail’s behavior was reasonable”,
“Abigail’s behavior was unethical”, “Abigail’s behavior was
immoral”, “Abigail’s behavior was appropriate”, and “Abigail’s
behavior was unacceptable”.
Part three of the questions were based on how would
participants advise Abigail and how they would respond if they
mistakenly received the answer key. In this part, statements
were divided, for example statements 1 to 3 are related to the
advice they would give Abigail (“I would advise Abigail to keep
silent”, “I would try to comfort Abigail”, and “I would give
Abigail the same advice that her friends gave her”). Statements
4 and 5 are based on how the participant would respond if they

6. were in the same situation. Part four asked for the participant’s
demographic information, including gender, age, ethnicity, their
first language, and whether they were a student from Florida
International University. Concluding the study, the participants
were asked to respond what feedback did Abigail’s friends give
her in general.
We had several dependent variables in our study, but despite of
these, we were more involved on the perceived behavior of
Abigail and the opinions of the participants if her behavior was
wrong, understandable, or reasonable.
Results Study One
Using our study conditions (supported vs. opposed vs. mixed)
and our independent variable, which was the impressions
towards Abigail’s behavior, we use a chi-square. We saw that
the chi square was significant, X2(4) = 147.04, p < .001. Most
Support participants recalled seeing supportive friends
comments (82.2%). Most Oppose participants recalled seeing
oppositional friend comments (81.4%). Finally, most of the
Mixed participants recalled seeing an average (81.1%). These
findings indicate that participants saw our original study
outcome manipulation as we intended. See Appendix 2.
In order to support our hypothesis of Abigail’s behavior, we
performed some other observations. In this case, the first One-
Way ANOVA test showed big differences among our
independent variable, the scenario conditions (supported,
opposed, or mixed) and our dependent variable, showed that
Abigail’s behavior was wrong, F(2,135) = 5.81, p = .005. The
Tukey post hoc test was conducted demonstrating that
participants were more likely to support the Abigail’s behavior
in the opposed condition (M = 3.95, SD = 0.95) than in the
feedback supported condition (M = 3.33, SD = 0.73). However,
the tests showed that there were no a big difference in the
mixed condition (M = 2.80, SD = 0.10) compare to opposed
condition. See Appendix 4.
Concluding, we ran an independent samples t-Test to check if
the participants would give Abigail the same advice that her

7. friends gave her, which the result was significant, t (89) = -
0.335, p < .01. Participants tended to support more giving the
same advice (M = 4.35, SD = 0.71) rather than opposed that
decision (M = 4.40, SD = 0.78). See Appendix 3.
Discussion Study One
Our observations were clear enough to prove that our hypothesis
was correct. Participants in the support condition supported
more the idea that the action of Abigail was understandable and
appropriate, compared to the participants in the opposite
condition, but not to mention that the participants in the
condition mixed were divided at both ends. However,
participants in supported and opposite conditions agreed that
they would give Abigail the same advice her friends gave him.
Study Two
Despite the fact that people of any gender could be exposed to
any kind of negative or pissing comments, the search and
exchange of knowledge occur continuously on social media
platforms, and individual users of social media receive
information based on the pages to which they subscribe
(Perfumi et al., 2019). However, the challenge of sharing and
receiving information faces the challenge of spreading fake
news by users who hide their identity. The situation of
spreading false news, however, does not mean the absence of
social norms on platforms. The perception of standards,
however, varies based on several factors, some of which include
the type of platform, anonymity, and the nature of relationships
between friends (Perfumi et al., 2019).
For some reasons, social media is provided to inform the
differences between the perspectives of men and women,
Idemudia et al. (2017). That is why it should be noted that
women have a predisposition to assume passive roles in the
media. Idemudia et al. (2017) affirm that gender occupies a
special place in the generation of an understanding of people's
decisions regarding the adoption and use of new technologies.
On the contrary, men enjoy more time for the use of social
networks, mainly due to the nature of their roles and social

8. expectations. Therefore, the opinions of the majority are likely
to influence women in determining an individual's behavior on
social media platforms.
According to Knobloch-Westerwick (2007) moral judgment is
generally influenced by factors that can be considered irrelevant
and insignificant in some way, an example is the influence of
fury. In context, an action that a happy person can describe as
morally correct may be viewed differently by the same person in
a state of rage and anger. In turn, Knobloch-Westerwick (2007)
also posits that "... gender differences have repeatedly emerged
in mood management research." There, the observation is that
men do not meet the predictions of mood management, while
women select messages according to the theory of mood
management. The influence of the insignificant factors,
mentioned above, on judgment tends to be high among women
compared to men.
The evaluation of a morality judgment by gender members,
therefore, would require one to consider the emotional situation
they were in at the time they made the judgment. However, the
factor that can increase women's judgment confidence is that
they have the ability to avoid feelings of anger and frustration
with the help of distraction (Knobloch-Westerwick, 2007).
Similarly, gender members are more likely to allow social
consensus to take precedence at the expense of norms of
rationality. Generally, conformity with social consensus creates
room for error and confusion to reign over a group. To avoid
confusion, women members of society should be encouraged to
avoid taking a passive position, as is the case in the media
sector.
Social Media users often tend to rate the acceptability of user
behavior based on the comments they receive from other users.
For example, comments unanimously support the user's
subsequent impact on participants to believe that the behavior
of the recipient of the comments is acceptable, while the
opposite is true. The claim, however, is that perceptions of a
user's behavior tend to be different between male and female

9. people. By way of illustration, while women can quickly
conclude that a user's behavior, and posts therein, are
acceptable based on the opinions and comments of other users,
men may exhibit a different perspective. In this case, a sizeable
population of men should not necessarily approve of a user's
behavior despite approving comments from a variety of users.
Greenwood and Lippman (2010) state that the strongest gender
differences exist, among other factors, in media representation,
content and selective exposure patterns (Greenwood and
Lippman, 2010).
On the other hand, users of social networks that are considered
synonyms tend to ignore people's opinions about their messages
and / or publications on social networks. To support the point of
view, Kasahara (2017) states that "women do not reveal
themselves with sensitive information and data to strangers."
Similarly, the empiricist claims that, compared to men, women
tend to hide their personal information and identities in online
domains and on social media. The implication is that
anonymous users may not feel a significant impact of cyber
bullying. Other factors that may contribute to women's more
privacy situation include security, privacy, and social roles /
pressures.
In general, social influence consists of some distinguishable and
conceivable differences, some of which are normative social
influence and informational social influence. The first refers to
the influence to meet the expectations that people in society
appreciate, while the second is the tendency to accept the
information provided by people as evidence to support reality.
When both cases apply to the context of a product, the
information provided must be uniform in terms of product
quality and must have a direct impact on the evaluation of
consumers Knobloch-Westerwick (2007). In terms of gender,
women tend to exhibit both variants, therefore, they provide
uniform information on the evaluation of the factors of interest.
Capacity is not in question and Knobloch-Westerwick (2007)
states that women have a high tendency to ruminate compared to

10. men. Interpersonal response orientations refer to the ways in
which people commonly respond to others. Women generally
exhibit the ability to balance orientations compared to men.
However, social influence in his case operates in environments
devoid of strong regulatory pressures.
According to the aforementioned reports, there are significant
differences between gender in the use and presence on social
networks. That is why we wanted to evaluate the probability
that misbehaviors named in social networks, such as Facebook,
are more accepted, depending on the gender that publishes it. If
we test a study related to gender and its support through social
networks, we could predict that participants who read
unanimously supportive feedback will rate the Facebook user’s
conduct as more acceptable than participants who read mixed
feedback.
More specifically, participants in the unanimously supportive
condition will more strongly agree with supportive survey
statements (“Abigail's / Adam's behavior was understandable,“
Abigail's / Adam's behavior was reasonable ”,“ Abigail's /
Adam's behavior was appropriate ”,“ I would advise Abigail /
Adam to keep silent ”, and“ I would try to comfort Abigail /
Adam ”) in comparison to the mixed condition.
Method Study Two
Participants
Two hundred students from Florida University were inducted to
participate in study two for the study and the sample size is one
hundred and forty for the study. Among 200 students 41% were
male and 56.5% were female, total male respondent are (n=82)
and female respondents are (n=113), only three participants
considered their gender as other. Ages ranged from a minimum
of 14 to a maximum of 83. Participants consist of a population
of 61.5% Hispanic American (n=123), African Americans were
19% (n=38), Caucasians were 14% (n=28) and Native Indians
were 0.5% (n=1). While some participant of others race were
4% ( n=8). See Appendix 6.
Materials and Procedure

11. Participants were asked verbally or otherwise, to participate in
an online study with the purpose to conduct a research. Once
the participant agreed to participate, he or she was directed to
the survey developed through Qualtrics software. In order to
follow standardized guidelines participants were notified of the
risk and benefits of participating in the study before the
attempted to the research material. Once they confirmed their
approval, they were able to continue with the survey, which
consisted of four sections.
In section one of the study, participants were manipulated
without noticing that each one was part of three different groups
“Support”, “Oppose”, and “Mixed”. All of them were given a
series of statements in order to see their impressions towards
Abigail and her cheating behavior as well as whether they agree
with her friends advices. While reading each statement, they
were asked to agree or disagreed using a scale from one
(strongly disagree) to six (strongly agree).
In section two of the study, participants read one of two
scenarios of a Facebook owner who cheat in an exam. These
scenarios were identical to the one we used for study one, but in
this case we changed the gender for Facebook owner to a male.
In this study, however, we omitted the oppose condition, due to
facts that it did not has a big difference compare with the
supported condition. Similar to study one, participants
continued with section two of the study, which asked them to
rate their impressions of the Facebook owner's test-taking
behavior. Once they completed the seven statements, they
proceed with the third part of the study, which once again, it
was similar to our prior study. They now were asked to rate
twelve statements about how they could advise the Facebook
owner, how they would respond if they mistakenly received the
answer key from the professor, and then generally rate the
Facebook owner.
Section four of the study asked for some demographic
information about the participants, including their gender, age,
race/ethnicity, their first language, whether they were a student

12. from Florida International University and their relationship
status. Concluding the study, the participants were asked to
respond what feedback did Facebook owner’s friends give her in
general and what they think the gender of the Facebook page’s
owner was.
Although of the several dependent variables we had, our main
objective was to perceive the behavior of the Facebook owner
and the opinions of the participants if his/her behavior was
wrong, and if the participants would give them the same advise
that their friends gave them.
Results Study Two
We pay closely attention whether the wrong behavior of the
Facebook owner received supported or mixed feedback. Using
our condition as our independent variable, we ran a
manipulation check We saw that the chi square was not
significant, X2(2) = 35.20, p < .001. Most Support participants
recalled seeing supportive friends comments (61%). While,
most of the Mixed participants recalled seeing an average
(71%). Phi showed a small effect, due to the fact that we
eliminated our opposed condition, doing that participants
showed more interest in this study. See Appendix 7.
In order to test our first dependent variable, we ran a 2 X 2
factorial ANOVA with our Comment Condition (Supportive vs.
Mixed), and Facebook Cheater Gender (Male vs. Female) as our
independent variables and the perceived of their behavior was
wrong as our dependent variable. Our results depicted that there
is not a significant main effect for comment condition on the
wrong behavior, F(1, 196) = 1.18, p > .05, meaning that there
was not differences between the Mixed Comments (M = 4.00,
SD = 1.36) and Supportive Comments (M = 4.21, SD = 1.37).
Analyzing our Gender Condition results, we can say there was
no main effect, F(1, 196) = .067, p >.05, with Male Facebook
cheater (M = 4.08, SD = 1.37) not differing from Female
Facebook Cheater (M = 4.13, SD = 1.36). See Appendix 8.
Since there was no effect between (Supportive vs. Mixed
comments and Male vs Female Gender) and the dependent

13. variable, we exanimated the interactions between them. Simple
tests showed that there was no interaction of Gender and the
scenario condition, F(1, 98) = .13, p > .05, with no differences
between Male Cheater (M = 4.16, SD = 1.50), and Female
Cheater (M = 4.26, SD = 1.23). Simple tests also showed there
was no interaction with Comment Condition, F(1, 98) = .34, p >
0.5, depicting once again no difference between Supportive
Comments (M = 4.16, SD = 1.50) and Mixed Comments (M = 4,
SD = 1.23). See Appendix 9.
For our second dependent variable, we did another 2 X 2
ANOVA with our same independent variables Comment
Condition (Supportive vs. Mixed), and Facebook Cheater
Gender (Male vs. Female), but now our dependent variable now
was “I would give them the same advise that their friends gave
them”. Results demonstrated a significant main effect for the
comment condition for giving the same advice, F(1, 196) =
5.12, p < .05. Participants seems to have Mixed comments in
regards they would give the same advise that the Facebook
owner’s friends gave them (M = 3.95, SD = 1.64) than support
the idea of giving them the same advise that their friends gave
them (M = 3.46, SD = 1.40). However, there was not a
significant main effect for the gender condition, F(1, 196) =
.053, p > .05, with Male Facebook cheater (M = 3.73, SD =
1.56) not showing a big difference compared to the Female
Facebook Cheater (M = 3.68, SD = 1.52). See Appendix 10.
Discussion Study Two
Our observations demonstrated that our predictions were wrong.
Participants in the support and mixed conditions think that the
Facebook owner’s behavior was wrong. Even when we
manipulated the gender of the Facebook owner and performed
some other simple test there was no interaction between our
independent variables and dependent variable. However, our
observations for our second variable “I would give them the
same advise that their friends gave them” participants had more
mixed feedback than supportive.

14. References
Cohen, J. B., & Golden, E. (1972). Informational social
influence and product evaluation. Journal of Applied
Psychology, 56(1), 54.
Greenwood, D. N., & Lippman, J. R. (2010). Gender and media:
Content, uses, and impact. In Handbook of gender research in
psychology (pp. 643-669). Springer, New York, NY.
Idemudia, E. C., Raisinghani, M. S., Adeola, O., & Achebo, N.
(2017). The effects of gender on the adoption of social media:
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ts_of_Gender_On_Social_Media_Adoption_The_Effects_of_Ge
nder_On_The_Adoption_of_Social_Media_An_Empirical_Invest
igation
Kasahara, G. M. (2017). Gender Differences in Social Media
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https://pdfs.semanticscholar.org/0379/2756cb77f4f637c3133cf3
4eb9702bcceeb5.pdf
Knobloch-Westerwick, S. (2007). Gender differences in
selective media use for mood management and mood

15. adjustment. Journal of Broadcasting & Electronic Media, 51(1),
73-92.
Kundu, P., & Cummins, D. D. (2013). Morality and conformity:
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Appendices
Appendix 1: Demographics – Study One
Race
Frequency
Percent
Valid Percent
Cumulative Percent
Valid
Caucasian
36
25.7
25.7
25.7
Hispanic
56
40.0
40.0
65.7
Native Indian
3
2.1

16. 2.1
67.9
African American
24
17.1
17.1
85.0
Asian American
9
6.4
6.4
91.4
Other
12
8.6
8.6
100.0
Total
140
100.0
100.0
Gender (1 = M, 2 = F)
Frequency
Percent
Valid Percent
Cumulative Percent
Valid
Male
62

17. 44.3
45.9
45.9
Female
73
52.1
54.1
100.0
Total
135
96.4
100.0
Missing
System
5
3.6
Total
140
100.0
Condition (1 = Support, 2 = Oppose, 3 = Mixed) * Attention
Check (1 = Support, 2 = Oppose, 3 = Mixed) Crosstabulation
Attention Check (1 = Support, 2 = Oppose, 3 = Mixed)
Total
Feedback supported her behavior
Feedback opposed her behavior
Feedback was mixed

18. Condition (1 = Support, 2 = Oppose, 3 = Mixed)
Support
Count
37
1
7
45
% within Condition (1 = Support, 2 = Oppose, 3 = Mixed)
82.2%
2.2%
15.6%
100.0%
Oppose
Count
1
35
7
43
% within Condition (1 = Support, 2 = Oppose, 3 = Mixed)
2.3%
81.4%
16.3%
100.0%
Mixed
Count
5
4
39
48

19. % within Condition (1 = Support, 2 = Oppose, 3 = Mixed)
10.4%
8.3%
81.3%
100.0%
Total
Count
43
40
53
136
% within Condition (1 = Support, 2 = Oppose, 3 = Mixed)
31.6%
29.4%
39.0%
100.0%
Appendix 2: Crosstabs and Chi-Square - Study One
Chi-Square Tests
Value
df
Asymptotic Significance (2-sided)
Pearson Chi-Square
147.039a
4
.000
Likelihood Ratio
142.630
4
.000
Linear-by-Linear Association
62.028

20. 1
.000
Appendix 3: T-test and statistics – Study One
Independent Samples Test
Levene's Test for Equality of Variances
t-test for Equality of Means
F
Sig.
t
df
Sig. (2-tailed)
Part III: I would give Abigail the same advice that her friends
gave her
Equal variances assumed
.759
.386
-.335
89
.739
Equal variances not assumed

21. -.334
87.697
.739
Group Statistics
Condition (1 = Support, 2 = Oppose, 3 = Mixed)
N
Mean
Std. Deviation
Std. Error Mean
Part III: I would give Abigail the same advice that her friends
gave her
Support
46
4.3478
.70608
.10411
Oppose
45
4.4000
.78044
.11634
Appendix 4: ANOVA and Descriptive Statistics – Abigail’s
behavior was wrong – Study One
ANOVA

22. Part II: Abigail's behavior was wrong
Sum of Squares
df
Mean Square
F
Sig.
Between Groups
9.434
2
4.717
5.811
.004
Within Groups
107.970
133
.812
Total
117.404
135
Descriptive
Part II: Abigail's behavior was wrong
N
Mean
Std. Deviation
Std. Error
95% Confidence Interval for Mean
Minimum
Maximum

23. Lower Bound
Upper Bound
Support
46
3.3261
.73195
.10792
3.1087
3.5434
2.00
6.00
Oppose
41
3.9512
.94740
.14796
3.6522
4.2503
1.00
6.00
Mixed
49
3.7959
.99957
.14280
3.5088
4.0830
2.00
6.00

24. Total
136
3.6838
.93256
.07997
3.5257
3.8420
1.00
6.00
Multiple Comparisons
Dependent Variable: Part II: Abigail's behavior was wrong
Tukey HSD
(I) Condition (1 = Support, 2 = Oppose, 3 = Mixed)
(J) Condition (1 = Support, 2 = Oppose, 3 = Mixed)
Mean Difference (I-J)
Std. Error
Sig.
95% Confidence Interval
Lower Bound
Upper Bound
Support
Oppose
-.62513*
.19352
.004
-1.0838
-.1665
Mixed
-.46983*

25. .18497
.033
-.9083
-.0314
Oppose
Support
.62513*
.19352
.004
.1665
1.0838
Mixed
.15530
.19070
.695
-.2967
.6073
Mixed
Support
.46983*
.18497
.033
.0314
.9083
Oppose
-.15530
.19070
.695
-.6073
.2967
Appendix 6: Demographics – Study Two
Statistics
What is your age?

26. What is your gender?
What is your race/ethnicity? - Selected Choice
N
Valid
199
198
198
Missing
1
2
2
Mean
28.61
1.60
2.41
Median
24.00
2.00
2.00
Mode
22
2
2
Std. Deviation
17.701
.521
1.183
Minimum
14
1
1
Maximum
221
3
6

27. Demographic - What is your gender?
Frequency
Percent
Valid Percent
Cumulative Percent
Valid
Male
82
41.0
41.4
41.4
Female
113
56.5
57.1
98.5
Other
3
1.5
1.5
100.0
Total
198
99.0
100.0
Missing
System
2
1.0

28. Total
200
100.0
Demographic - What is your race/ethnicity?
Frequency
Percent
Valid Percent
Cumulative Percent
Valid
Caucasian
28
14.0
14.1
14.1
Hispanic
123
61.5
62.1
76.3
Native Indian
1
.5
.5
76.8

29. African American
38
19.0
19.2
96.0
Others--Please specify
8
4.0
4.0
100.0
Total
198
99.0
100.0
Missing
System
2
1.0
Total
200
100.0

30. Appendix 7: Crosstabs and Chi Square – Study Two
Comment Condition (1 = Support, 2 = Mixed) * Without
looking back, what general feedback did the Facebook owner's
friends give them? Crosstabulation
Without looking back, what general feedback did the Facebook
owner's friends give them?
The feedback supported their behavior
Feedback was mixed
Unknown
Comment Condition (1 = Support, 2 = Mixed)
Supportive Comments
Count
61
29
10
% within Comment Condition (1 = Support, 2 = Mixed)

31. 61.0%
29.0%
10.0%
% within Without looking back, what general feedback did the
Facebook owner's friends give them?
70.9%
29.0%
71.4%
% of Total
30.5%
14.5%
5.0%
Mixed Comments
Count
25
71
4
% within Comment Condition (1 = Support, 2 = Mixed)
25.0%
71.0%
4.0%
% within Without looking back, what general feedback did the
Facebook owner's friends give them?
29.1%
71.0%
28.6%

32. % of Total
12.5%
35.5%
2.0%
Total
Count
86
100
14
% within Comment Condition (1 = Support, 2 = Mixed)
43.0%
50.0%
7.0%
% within Without looking back, what general feedback did the
Facebook owner's friends give them?
100.0%
100.0%
100.0%
% of Total
43.0%
50.0%
7.0%
Chi-Square Tests
Value
df
Asymptotic Significance (2-sided)
Pearson Chi-Square
35.281a
2
.000
Likelihood Ratio

33. 36.400
2
.000
Linear-by-Linear Association
12.088
1
.001
N of Valid Cases
200
Appendix 8: ANOVA Their Behavior Was Wrong – Study Two
Descriptive Statistics
Dependent Variable: Their behavior was wrong
Comment Condition (1 = Support, 2 = Mixed)
Facebook Cheater Gender (1 = Male, 2 - Female)
Mean
Std. Deviation
N
Supportive Comments
Male Facebook Cheater
4.16
1.503
50
Female Facebook Cheater
4.26
1.226
50

34. Total
4.21
1.365
100
Mixed Comments
Male Facebook Cheater
4.00
1.229
50
Female Facebook Cheater
4.00
1.485
50
Total
4.00
1.356
100
Total
Male Facebook Cheater
4.08
1.368
100
Female Facebook Cheater
4.13
1.361
100
Total
4.11
1.361
200

35. Tests of Between-Subjects Effects
Dependent Variable: Their behavior was wrong
Source
Type III Sum of Squares
df
Mean Square
F
Sig.
Corrected Model
2.455a
3
.818
.438
.726
Intercept
3370.205
1
3370.205
1803.134
.000
CommentCondition
2.205
1
2.205
1.180
.279
GenderCondition
.125
1
.125
.067
.796

36. CommentCondition * GenderCondition
.125
1
.125
.067
.796
Error
366.340
196
1.869
Total
3739.000
200
Corrected Total
368.795
199
a. R Squared = .007 (Adjusted R Squared = -.009)
Appendix 9: Simple Tests - Their Behavior Was Wrong – Study
Two
Descriptive Statistics
Dependent Variable: Their behavior was wrong
Comment Condition (1 = Support, 2 = Mixed)
Mean
Std. Deviation
N
Supportive Comments
4.26

37. 1.226
50
Mixed Comments
4.00
1.485
50
Total
4.13
1.361
100
a. Facebook Cheater Gender (1 = Male, 2 - Female) = Female
Facebook Cheater
Tests of Between-Subjects Effects
Dependent Variable: Their behavior was wrong
Source
Type III Sum of Squares
df
Mean Square
F
Sig.
Corrected Model
1.690b
1
1.690
.912
.342
Intercept
1705.690
1
1705.690
920.370

38. .000
CommentCondition
1.690
1
1.690
.912
.342
Error
181.620
98
1.853
Total
1889.000
100
Corrected Total
183.310
99
a. Facebook Cheater Gender (1 = Male, 2 - Female) = Female
Facebook Cheater
b. R Squared = .009 (Adjusted R Squared = -.001)
Descriptive Statistics
Dependent Variable: Their behavior was wrong
Facebook Cheater Gender (1 = Male, 2 - Female)
Mean
Std. Deviation
N
Male Facebook Cheater

39. 4.00
1.229
50
Female Facebook Cheater
4.00
1.485
50
Total
4.00
1.356
100
a. Comment Condition (1 = Support, 2 = Mixed) = Mixed
Comments
Tests of Between-Subjects Effects
Dependent Variable: Their behavior was wrong
Source
Type III Sum of Squares
df
Mean Square
F
Sig.
Corrected Model
5.684E-14b
1
5.684E-14
.000
1.000
Intercept
1600.000

40. 1
1600.000
861.538
.000
GenderCondition
.000
1
.000
.000
1.000
Error
182.000
98
1.857
Total
1782.000
100
Corrected Total
182.000
99
a. Comment Condition (1 = Support, 2 = Mixed) = Mixed
Comments
b. R Squared = .000 (Adjusted R Squared = -.010)
Descriptive Statistics
Dependent Variable: I would give them the same advice that
their friends gave them
Comment Condition (1 = Support, 2 = Mixed)

41. Facebook Cheater Gender (1 = Male, 2 - Female)
Mean
Std. Deviation
N
Supportive Comments
Male Facebook Cheater
3.52
1.542
50
Female Facebook Cheater
3.40
1.262
50
Total
3.46
1.403
100
Mixed Comments
Male Facebook Cheater
3.94
1.570
50
Female Facebook Cheater
3.96
1.714
50
Total
3.95
1.635
100
Total
Male Facebook Cheater

42. 3.73
1.563
100
Female Facebook Cheater
3.68
1.523
100
Total
3.71
1.539
200
Appendix 10: ANOVA I would give them the same advice that
their friends gave them – Study Two
Tests of Between-Subjects Effects
Dependent Variable: I would give them the same advice that
their friends gave them
Source
Type III Sum of Squares
df
Mean Square
F
Sig.
Corrected Model
12.375a
3
4.125
1.761
.156
Intercept
2745.405
1
2745.405

43. 1171.768
.000
CommentCondition
12.005
1
12.005
5.124
.025
GenderCondition
.125
1
.125
.053
.818
CommentCondition * GenderCondition
.245
1
.245
.105
.747
Error
459.220
196
2.343
Total
3217.000
200
Corrected Total
471.595
199

44. a. R Squared = .026 (Adjusted R Squared = .011)