mobile social media in social internations final paper

MOBILE SOCIAL MEDIA IN SOCIAL INTERACTIONS

An Applied Behavior Analysis: Reducing Mobile Social Media Application Use in the

Presence of Others

By: Kelsey Harris

Elon University

Senior Psychology Seminar 2013

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Abstract

This study used applied behavior analysis to change a participant’s use of mobile

social media applications while in the presence of other people. The participant was

in fact the researcher. During baseline, the participant recorded data without

making any changes to the normal daily routine. The intervention involved placing

the cellular device out of sight during social interactions, as well as having to place a

$1 bill into a plastic bag for every time the social media application was accessed.

The intervention immediately decreased the number of times mobile social media

was used during social interactions with others. The results suggest that mobile

social media application use can be decreased while in the presence of others, and

maintained over a significant period of time.

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An Applied Behavior Analysis: Reducing Mobile Social Media Application Use in the

Presence of Others

Technology has become a huge role in the lives of people today. So much, that

researchers have wondered if technology has negatively impacted communication

skills and interpersonal skills (Reich, Espinzoa & Subrahmanyam, 2012). With the

recent advancements of the Internet and cell phone use, mobile technology has

entered into a completely different realm of every day life. The rise of mobile

applications within the past decade has allowed an instant gratification for Internet

and social media users.

First, there is a need to differentiate the role of the Internet into its various

uses. Internet usage can include solitary, non-social activities such as browsing,

video games, and video streaming, but can also include social interactions through

instant messaging, e-mail, social networks, and video chat. While the choice is based

on individual preferences, there is evidence that online solitary activities are

harmful in building social relations with others (Baym, Zhang, & Lin, 2004).

Social Media

Sixty-five percent of adults in 2011 used social networking sites on the

Internet, a 35% increase compared to 2008. Today, Facebook, Twitter, and

Instagram are three of the most used social media applications by populations

(Wang, Tchernev, & Solloway, 2012). While all three share some similarities, they

also share many key differences.

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Facebook is recognized as an online yearbook for people all over the world,

known to bring old friends back together after life changes force them apart. As one

of the most used social networks among college campuses, Facebook has become a

universal outlet for identify exploration. However, Facebook use has been

negatively correlated with initiating or starting an interpersonal relationship

(Jenkins-Guarnieri, Wright, & Hudiburgh, 2012).

College-aged individuals are faced with many challenges – adapting to new

environments, new friends, academic responsibilities, identity development, and

future career planning. While some students thrive in high-pressure environments,

others may fall victim to depression and anxious thoughts about themselves.

Facebook has been used excessively as a means for social support, mainly among

college students with depressive or anxious thoughts (Koc & Gulyagci, 2013).

Facebook use is also negatively related to college student engagement, or the real-

world effort to participate in the world to actively reach goals. Research such as this

has prompted professors and educational personnel to discover ways to infuse

Facebook and other social network sites positively into the classroom (Junco, 2012).

Twitter is another medium of social media that is specifically designed for

short bursts of information, in 140 characters or less. Many news outlets, including

CNN, NY Times, ESPN, and USATODAY, have taken to Twitter to reach the masses at

a faster rate. While researchers believe that Twitter has allowed its users to take an

even closer look into the lives of others (even celebrities), many believe that this

multidimensional level has created another false “world” that Twitter users fall into

(Murthy, 2012).

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While there has been significantly less research done on Twitter and

Instagram as opposed to Facebook, there are implications that both mediums have

the same effect on users, as does Facebook. Instagram provides a medium for users

to share millions of pictures instantly with friends, family members, and strangers

alike. The application seemingly has people capturing every moment with their cell

phones, making every moment count (Hochman & Scwarts, 2012).

Internet addiction is a phenomenon urging concern with the growing

number of Internet and social media users. While there is no consensus on a

singular operational definition of Internet addiction, there are several factors that

have been identified to be components. The six indicators include salience, mood

change, tolerance, withdrawal symptoms, conflict, and relapse/reinstatement.

Salience is among one of the first stages when the Internet becomes one of the most

central parts of a person’s day. Mood change then accompanies use or disuse of the

activity, followed by tolerance and withdrawal symptoms. The person will

continually need higher doses of Internet usage to satisfy their addiction, and if

these high doses are not met, withdrawal symptoms occur. Conflict, both

interpersonal and intrapersonal, follows next and threatens their offline social life

(family, friends, peers, etc.). The last step, relapse/reinstatement, involves the

person referring back to their addictive behaviors, even after short periods of

restraint (Smahel, Brown, & Blinka, 2012). Wang et al. (2012) researched social

media as a vessel that fulfills our emotional, cognitive, social, and habitual needs on

a daily basis. While data suggests that social media is gratifying all-around,

researchers found that heavy social media use can actually lead to increasing needs

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that become harder and harder to gratify. State of mind, individuality, amount of

use, and reasons for using social media are important factors relating to the

gratifications and personal needs looking to be fulfilled.

Cell Phone Applications and Presence

Social media mobile applications such as Facebook, Instagram, and Twitter

are now a part of a billion dollar industry for cell phones, and continuing to grow at

a rapid pace. From 2009 to 2010, the number of mobile application Facebook users

increased by 112%, while the number of mobile application Twitter users increased

by 347% (Anderson, n.d.).

The ever-impeding presence of cell phones has combined both the virtual

space and the physical, present world. This has led to cell phones being used

frequently during face-to-face social interactions, such as at the dinner table or

social outings, because individuals tend to think of other people and happenings

outside of their present context. Before the cell phone, it could be said physical

conversations had a higher quality, as the virtual world was less likely to interfere

with the present (Pszybylski & Weinstein, 2012). In some countries, loud cell phone

conversations in public areas have become such a nuisance that mobile technology

is banned from a respective area (i.e. movie theaters, plays, etc.). Some countries

such as France, China, Russia, and Israel permit the use of jammers, devices that

block mobile phone signals in a particular area (Srivastava, 2010). Przybylski and

Weinstein (2012) found that just the presence of cell phones can decrease the

feelings of closeness and trust during a face-to-face conversation, and decreases it

even more during meaningful, intimate conversations. The researchers contribute

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this effect to the virtual world “colliding” with the present world. Meanwhile, in

2004, college students indicated that only 27% of online interaction took place

while in the presence of other people. This number has greatly increased in the past

decade (Baym et al., 2004).

In order to keep up with the newest technologies, society has to keep

updating its social norms surrounding the technological advances. Humphreys

(2005), in his observations of public places, noticed that people who are alone tend

to use their cell phones as a way to deflect their feelings of vulnerability and to look

occupied. Meanwhile, people who are with others in a public place may also feel this

same vulnerability if their friend leaves the table, or engages in conversation with

someone else that the original friend does not know. The latter phenomenon has

become known as cross talk (Humphreys, 2005).

In another aspect, public areas such as subways, trains, and buses pose

situations where a majority of the individuals are alone. In today’s society, it has

become the norm for people to be on their cell phones, read newspapers/books, or

listen to music to occupy their time and vulnerability. This false sense of busyness

has deterred seemingly random conversations with strangers (Banjo, Hu, & Sundar,

2008). Among these public places, cell phones are also seen as distractions in the

workplace, classroom, drivers, and among pedestrians. The risk of pedestrian

accidents greatly increases when crossing the street while simultaneously using

mobile Internet applications. This type of multitasking causes the pedestrian to pay

less cognitive attention to their surroundings (Byington & Schwebel, 2013).

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Some gender differences have also been discovered in this area. Women

specifically use cell phones as a way to deflect unwanted attention from possible

suitors or strangers, more often then men (Srivastava, 2010). Kimbrough,

Guadagno, Muscanell, & Dill (2013) discovered that women are using social media

networks at a higher rate than men, and even used text-based cell phone

communication at a higher rate as well. Interestingly enough, both the men and

women in the study still acted within their gender roles regardless of the situation.

Women tended to use social media and text-based communication to maintain

relationships, while men tended to use it as a means to an end (Kimbrough et al.,

2013).

Interpersonal Development

Internet and cell phone usage has been found to serve several different

purposes in the lives of adolescents today. In 2012, seventy-five percent of 12-17

year-olds owned cell phones, indicating the large number of adolescents with their

own mobile device (Pea et al., 2012). Because adolescence is a key phase of the

developmental process, many researchers have focused on this age range and their

connection with Internet technology.

Reich et al. (2012) explored high school students’ Internet usage and found

that adolescents seem to use networking sites to supplement their offline

friendships and may, in fact, support emotional intimacy. Online networks also

allow people to widen their social contacts far greater than just in face-to-face

contexts (Reich et al., 2012). While there is a known risk that social networks

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increase interactions between strangers, research shows that a majority of teens are

actually proactive in their online safety.

Past research has also been done to explore the effects of high media use and

its developmental consequences on children. The younger children are exposed to

high media use (TV, cell phone, computer, social media), the higher the risk of being

less content in relation to friendships, relationships with parents, and feelings of

sadness/unhappiness (Rideout, Foehr, & Roberts, 2010). However, researchers do

recognize that the younger generation also feels a higher need for online social

interaction compared to older populations. Adolescents and college-aged people

may find that Internet interaction has a higher value than their parents or

grandparents might believe, and in turn incorporate the Internet into their social

lives more often than others (Wang et al., 2012).

However, while there are positive implications of Internet usage on

relationships, online communication and face-to-face communication cannot be

substituted for one another. Face-to-face communication is still an integral

component of social and emotional development. Having a high and frequent

amount of face-to-face communication is positively related to greater social success,

more feelings of normalcy, and more sleep among girls aged 8-18 (Pea et al., 2012).

This study is suggestive of the positive effects of face-to-face communication among

young adults, but does not establish true cause and effect.

Geographical context also plays a role in the frequency of face-to-face

communication as a medium. Baym et al. (2004) examined social relationships

among college students, and found that 74% of local relationships are maintained

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face-to-face, while long distance relationships are only 41% likely to use face-to-face

communication. Overall, lack of nonverbal cues, lack of emotional information, and

lack of physical interaction are all attributed to the weak “quality” of communication

from online mediums (Lee, Leung, Lo, & Xiong, 2011).

Applied Behavior Analysis

In this present study, the researcher took on the perspective of a behavior

analyst to explore the effects of mobile social media usage while with others in close

proximity. The researcher aimed to decrease the amount of social media usage

while in the presence of others, and ultimately maintain the change even after the

study. According to Chance (1998), behavior analysis can be defined as the study of

the functional relations between behavior and environmental events. Behavior

analysts focus on behavior change, and search for ways to evoke this change by

observing and manipulating the antecedents and consequences related to the

behavior. The term “antecedent” refers to the situations that happen before the

behavior occurs, while “consequence” refers to what happens after the behavior.

When performing applied behavior analysis, it is important to build a theory

around a data-based approach rather than theory testing. Behavior analysts

concentrate solely on the actual, observable behavior involved, and not the complex

issue underneath. Rather than looking at psychological reasons behind a behavior,

the data systematically provides behavior analysts with a process for treatment

(Bailey & Burch, 2009).

Given the recent boom in mobile social media applications, this present study

explored one participant’s use of mobile applications while in the presence of others

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as the target behavior. The researcher aimed to discover the antecedents and

consequences related to the particular behavior, and if the behavior could be

modified and decreased. This study can hopefully shed light on the younger

generations constant usage of mobile phone technology and reiterate the

importance of being in the “current” moment of conversations with others.

Method

Participants

One 21-year-old female undergraduate student participated in the applied

behavior analysis out of general interest in the topic. The participant recorded her

own behavior and implemented her own intervention. The participant is in fact the

researcher.

Apparatus

The participant recorded whether or not she initiated the mobile Facebook,

Instagram, or Twitter applications during a social interaction. Social interactions

were defined as contact with one or more other person in a social context for longer

than one minute. The recording system involved the participant marking an I (yes,

used social media) or X (did not use social media) in the Notes application located

on the IPhone, as shown in Figure 1. At the end of the day, the number of “yes”

tallies was divided by the total number of social interactions, giving a percentage for

each day of testing. A Ziploc bag, dollar bills, and Hershey’s with Almond bars were

also materials used during the intervention phase. The Ziploc bag served as a

container for the dollar bills, which were a punishment during the intervention

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phase. The Hershey’s with Almond bars were used as a reward and positive

reinforcement during intervention.

Procedure

Applied behavior analysis and modification were used solely for this

research. One participant tracked her own use of social media on cell phone

applications while in the company of others, for a 14-day period to serve as baseline

data. Every social interaction had to involve one or more other person. Social media

use was measured as the act of clicking or starting up either the Facebook,

Instagram, or Twitter application on her cell phone during the social interaction. It

is also important to note that if another person walked up and joined the

group/social interaction, it was then counted as another separate interaction than

the previous one. Example: An individual social interaction would be sitting at lunch

with two other friends. If another friend joins the group a few minutes later, it

would then be counted as two separate interactions (one before the friend joined,

one after they joined).

The intervention phase began once the baseline data collection period ended

after fourteen days. The stimulus control involved keeping the IPhone out of sight

during the social interaction, either by placing the device in a book-bag or a purse.

To positively reinforce the intervention, if the percentage of ‘YES’ interactions fell

below 30% three days in a row, the participant was allowed to have a Hershey’s

with Almond bar. As a punishment for every ‘YES’ interaction at the end of the day,

the participant had to place a $1 bill into a Ziploc bag and not touch it until the

conclusion of the study.

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Results

Figure 2 shows the percentage of social interactions where mobile social

media applications were opened and accessed by the participant. The baseline

phase lasted a total of 14 days, while the intervention phase lasted 44 days. The

intervention phase is defined by keeping the cell phone out of sight during social

interactions with others. The dotted line in Figure 2 signifies the end of baseline

collection and the beginning of the intervention phase.

The two phases depicted two different patterns of data, seen through various

descriptive statistics in Table 1. During the baseline phase, the mobile social media

applications were accessed in an average of 67% of all social interactions, while in

the intervention phase the applications were opened in only an average of 24% of

social interactions. Baseline data collection also has a median of 75%, while the

intervention data has a median of 24%. Lastly, the mode of the baseline data

collection is 100%, while the mode of the intervention phase is 0%. The descriptive

statistics in Table 1 highlight the decrease between phases in mobile social media

use while in the presence of others.

However, there were also significant outliers that stood out in both the

baseline and intervention phase in Figure 2. During baseline, the first five days

showed a consistent percentage on or above 50. Days 6, 7, and 8 then dropped

below 50%, and returned back to 50% and above from days 9 to 14. The beginning

of the intervention phase showed an immediate and dramatic decrease in

percentage (100% day 14; 33% days 15 and 16). Throughout intervention, the

percentages consistently stayed on or below 50% except for days 27 and 35.

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Discussion

Results indicated that the intervention successfully decreased the number of

times mobile social media was accessed during a social interaction, as expected. On

average, the intervention produced a 43% decrease compared to baseline.

The participant noticed that the actual act of keeping the cell phone out of

sight during social interactions was not tough. Rather, remembering to keep the

phone out of sight was the hardest part of conducting the intervention. Because the

cell phone is such an integral part of the participant’s life, it became a conscious act

to not automatically reach for it. Also mentioned during the intervention, the dollar

bills as a punishment effectively deterred the participant from wanting to commit

the target behavior, while the Hershey’s with Almonds bars created a valued

reward.

When assessing the contingency of the target behavior with antecedents and

consequences, the participant noted that the occurrence of the behavior was related

to the characteristics of her companions in the social context, lulls and pauses in

conversation, social contexts revolved around eating/sitting down, and when her

companions also proceeded to look at their cell phones. Humphreys’ (2005)

research concerning cell phones and social norms is supported in this study, as the

pauses in conversation indicate the vulnerability of the companions to maintain

constant conversation and feel ‘awkward’ when it is not accomplished. Meanwhile,

groupthink is a psychological phenomenon that describes the participant’s need to

conform with the group, as her companions also pull out their cell phones during

the social interaction (Choi & Kim, 1999). Social contexts revolved around

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eating/sitting down were a perceived antecedent of the target behavior, but not an

absolute requirement. Sitting down during a social interaction creates an

environment of rest: a slight pause during a busy day of running from place to place.

During these restful periods, social interactions often last longer, and are therefore

more likely to have an increased chance at having a pause or lull in conversation

(Humphreys, 2005).

Consequences that occurred after the target behavior include feelings of guilt

and instant gratification from checking the social media applications. Instant

gratification can be most likely attributed to early signs of Internet addiction, which

may soon become the norm among the younger generation. Internet addiction

involves feelings of withdrawal and satisfaction from all aspects of the Internet,

including mobile applications (Smahel, Brown, & Blinka, 2012). The participant also

observed that the instant gratification was stronger if a post was made on social

media the day of – causing the participant to want to check the application more

often for ‘tweets, replies, or likes’. Invariably, the mobile application use seemed to

decrease the perceived meaning of the current conversation taking place in present

time; the cell phone “collided” with the real world (Przybylski & Weinstein, 2012).

There are two notable strengths within this applied behavior analysis: the

study only focused on only one participant for an extended amount of time, and the

intervention was successful in changing behavior. Focusing on one participant

allowed the study to be in depth and personal, as it was easier for the intervention

to inevitably be successful in changing the behavior. However, one important

limitation to mention is the study’s generalizability across participants. Because the

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intervention was personal to the participant due to the value of money and

Hershey’s with Almonds bars, the intervention may not hold the same weight with

other participants. While the overall concept of the study and change in behavior is

generalizable, the intervention may not be.

It is interesting to also note that reactivity most likely played a part in the

immediate and dramatic decrease during the first days of the intervention phase.

Reactivity occurs when participants change their behavior once they are aware that

they are being observed (Chance, 1998). In this study, the participant and

researcher are one and the same, thus leaving room for an even bigger reactivity

influence.

Future research studies surrounding mobile application use should focus on

the variety of companion characteristics. Companion characteristics could vary

according to age, strength of relationship/friendship, as well as family members

versus peers. In this study, the researcher noticed that the amount of social media

activity was significantly lower when the companions were older in age, family

members, and/or had a stronger relationship to the participant. This variability will

help determine if changes in relationship influence the use of mobile social media in

the presence of others.

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Table 1

Descriptive Statistics Baseline Intervention

Average 67% 24%

Median 75% 24%

Mode 100% 0%

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Figure 1. Snapshot of the recording system used in the Notes application on the

participant’s IPhone. The “1” signifies an interaction where the mobile social media

application was accessed, where an “X” represents an interaction where social

media was not accessed.

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Figure 2. This graph shows the percentage of interactions where social media applications were opened. The dotted line

signifies the end of baseline collection and beginning of the intervention phase.

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MOBILE SOCIAL MEDIA IN SOCIAL INTERACTIONS 24

mobile social media in social internations final paper

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