Social Media Use and Depression

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Social Media Use and Depression


Social media outlets like Twitter and Facebook are a relatively new phenomenon, but for many people, take up a large portion of free time. It is easy to spend hours scrolling through a feed, looking at pictures and other user-created content from friends and celebrities. The content we find on social media is important because it influences how we see ourselves and make sense of the world around us. People on social media interact differently than they would in person; there are different norms about what is socially acceptable, especially when it comes to humor. Reyes, Rosso, and Buscaldi (2012) analyzed the figurative humor used on social media and noted that understanding irony and humor on social media requires the ability to understand what is being said in context. This context is important for creating a sense of belonging that humans, as social creatures, crave. Meanwhile, other researchers have found correlations between mental health problems and social media use but have yet to uncover if one causes the other. The proposed study seeks to find if dark figurative humor about things like suicide on social media is detrimental to people, specifically with depression. A Plagiarized Student Sample: ORDER YOUR PAPER NOW

Social media is different from other media outlets like newspapers and television because its content is user-generated, for the most part. According to Perloff (2014), social comparison theory explains why social media might influence people more than other media outlets. This model essentially states that people are more heavily influenced by their peers, especially ones they share an aspect of their identity with. In the case of social media, user-generated content would be especially influential on users because of their ability to see people they have interacted with during their day to day lives represented online (Perloff 2014). In other words, social media is more personal than other media outlets. The social skill model of problematic internet use, described by Lee-Won, Herzog, and Park Sung (2015), is another way to narrow down who might be affected more by social media. This model presents the need for self-presentation, the need to maintain a positive reputation, as the primary driver behind social media use. Those who struggle with social interactions, such as people with depression or anxiety, may feel more comfortable expressing themselves online and are more likely to prefer social media over face-to-face interactions, leading to an increase in use (Lee-won and et al 2015). According to the model, these people will be more at risk for developing a reliance on social media. The social comparison theory and social skill model of problematic internet use create a connection between people with depression and problematic social media use. Because of a possible need for self-presentation that is not met through face-to-face interaction and social media’s ability to show them people they already relate to, people with depression may be more influenced by social media. If this social media use is detrimental or not is what the proposed study seeks to find out.

There is currently little consensus about the cause and effect relationship between mental health problems and social media use. According to a study by Naslund and et al (2017), people that identify themselves on social media as having a major mental illness believe that social media can be used as a resource for promoting healthier lifestyles, contacting doctors and healthcare providers, and finding support.  The significance of these findings is that people with health problems, including mental illnesses, feel comfortable finding support on social media and believe that even more resources should be made available. In a study by Reyes and et al (2012), the researchers surveyed people with health problems like diabetes about how they use social media in relation to their diseases. The study found that the amount of support the participants received from social media seemed to depend on how much offline support they received. As support from social media increased, the participants’ perceived support from the people in their lives decreased. This study reinforces the idea that the internet can be a place for people to connect with others that share similar problems. However, based on the social skill model of problematic internet use, this support system could potentially be detrimental to people that struggle with face-to-face interaction because it could result in increased use that causes them to become reliant on social media. A reliance on social media would in turn isolate them further from the people around them, causing further need for support on social media.

Although it has been found that there is a positive attitude toward social media being used as a support system for health problems, there have also been correlations between social media misuse and depression. In a study by Pantic and et al (2012), high school students were surveyed about their social media use and given the Beck Depression Inventory. A significant positive correlation was found between the time spent on social media and the score received from the Beck Depression Inventory. Although this correlation does not support the idea that social media use causes depression symptoms, the current study will use this correlation to justify studying whether depression is worsened by social media use, or if the reason there is a correlation is purely because people with depression are comfortable using social media as a support system. According to a study by Lee-won and et al (2015), addiction to social media positively correlates to depression. The study measured the relationship between participants’ addiction to social media and video games and their level of ADHD, OCD, depression, and anxiety. There was a low correlation between social media addiction and video game addiction, but both addictions positively correlated to measures of ADHD, OCD, depression, and anxiety. This is significant because it shows that while social media can be used as a place to find support for mental illness, people with mental health problems might be more likely to misuse social media, leading to worsening symptoms.

While purely the use of social media could isolate a person with depression from the people around them, it is possible that the actual content they are seeing is what causes their higher depression scores. Cavozos-Rehg and et al (2016) analyzed actual social media content about depression by coding tweets for the expression of symptoms of Major Depressive Disorder as put forth by the Diagnostic and Statistical Manual of Mental Disorders. The researchers found that most of the tweets were in support or trying to provide help to those with depression, but that almost as many tweets were about feeling symptoms of depression. This study supports the idea that people with depression are using social media to find support and help, especially on the social media outlet called Twitter. This, combined with the research of Reyes, et al (2012), supports a possible content-related issue with social media use. The effect of tweets about depression on people with depression has yet to be studied. On one hand, seeing other people struggling with the same thoughts and emotions might make a person with depression feel less alone. However, too much exposure to that could make a person feel as though there is no hope of getting better. Also, if people with depression feel as though depression is their only way of connecting with others, a positive feedback cycle of depressed feelings and instant social gratification may start, meaning the worse a depressed person feels, the more they feel connected to other people. A Plagiarized Student Sample: ORDER YOUR PAPER NOW

As described by Reyes, et al (2012), the figurative humor used by social media requires context. This context is important because it is possible that it makes people who are seeking social interaction feel more connected to those they find online than the people they see in person from day to day. The present study will focus primarily on the relationship between depression and Twitter content about depression to seek a causal relationship. To do this, participants’ depression will be measured before and after they are exposed to different levels of figurative humor about suicide or depression. The idea is that the figurative humor will provide a way for the participants to feel more connected to others with their same problems. However, if the participant is exposed to figurative humor about depression and suicide too much, it is possible they might feel as though everyone is facing their same problems and that there is no true solution or hope of getting better. The percent change in their depression scores will show if the humor in social media having to do with their own problems with depression causes a short-term worsening in depression symptoms or not. It is hypothesized that between the control group and the depressed group, the depressed group’s depression will increase more after exposure to the figurative humor about depression. Furthermore, participants exposed to the higher levels of figurative humor about depression will increase on the depression scale more than those exposed to low and medium levels. Finally, the depressed group exposed to the most figurative humor about depression will increase on the depression scale more than the depressed participants exposed to the medium and low figurative humor exposure.



The proposed study is a 2 (Type of Participant: Depressed vs Not Depressed) x 3 (Level of exposure to figurative humor about depression: low vs medium vs high) between-subject quasi experimental design.

The independent variable that the participants will be exposed to will be a sample of tweets made by the researchers that include different proportions of figurative humor about depression to random tweets obtained from Twitter. The proportion will determine which level of independent variable the participants are being exposed to. Of 100 tweets, 10 will be an example of figurative humor about depression for the low exposure group, 20 for the medium exposure group, and 30 for the high exposure group. The other 90, 80, or 70 tweets will be randomly obtained tweets from Twitter.  As there is no current research that has manipulated the experience of social media, these numbers will have to be adjusted based on initial tests to see how much figurative humor is noticeable and actually has an effect on users. In order to operationalize figurative humor on social media, tweets about depression that are meant to be humorous will be obtained from the data base created by Cavozos-Rehg and et al (2016). Only 30 total tweets about depression are needed, so these will be hand-picked by the researchers of the proposed study to represent figurative humor. According to Reyes, et al (2012), figurative humor requires context, so the tweets about depression that are chosen must have over 1,000 retweets, which signifies that people on social media have recognized the tweet as comical or relatable.

The dependent variable is the percent change in the depression score obtained after exposure to a level of the independent variable.

The quasi variable is whether or not a participant has depression. This will be assessed using the Beck Depression Inventory (Davies, Burrows, and Poynton (1975).


Participants will be given the Beck Depression Inventory, which was determined by Davies and et al (1975) to be a reliable and valid assessment of depression. The Beck Depression Inventory has 21 items consisting of 4 statements. The participant picks the statement that matches their symptoms the closest and receives a certain number of points that when added up, give them a score between zero and 63. The closer the score is to 63, the higher the level of depression. An example of the difference between two statements in an item would be zero points given for the statement “I do not feel sad” and 3 points given for “I am so sad and unhappy that I can’t stand it.” The data gained from the survey will be parametric because the scale of measurement is ratio.


A tweet calling for participants in a study will be sent out on Twitter and 1,000 participants will be randomly selected to come into the lab. In the present study, participants receiving a score of 17 or above will be placed in the depressed group, and those scoring between 16 and zero will make up the control group. This is because according to Davies and et al (1975), a score of 17 or above deems a person as having borderline clinical depression. People from both the control group and depression group will be randomly assigned to be exposed to the different levels of exposure to figurative humor about depression. Gender differences and socioeconomic status will be controlled for by randomizing who is chosen from the population of Twitter users.


Volunteers will be called for on Twitter to take part in this study. Participants will be brought in to a lab setting and given a consent form to sign that notifies them of their ability to leave the study at any time as well as their right to withdraw their data from the results. They will be told that the study is measuring depression. They will then be given a copy of the Beck Depression Inventory to complete. After they have completed the survey, they will be asked to read 100 tweets from an iPhone. Reading the tweets from an iPhone and not being told to read carefully will give the study as much mundane realism as possible in a lab setting. The participants will only be given 100 tweets so that they will theoretically not get bored. They will not be told which level of figurative humor about depression they will be exposed to and will be randomly assigned to either the low, medium, or high exposure group. Then, the participant will be given the Beck Depression Inventory again. Afterwards, they will be briefed on the true purpose of the study and offered access to a therapist to help them with any potential triggers. They will then be given a consent form allowing the researchers to use their data.

Proposed Statistical Analysis

The current study will use a between subject 2×3 Analysis of Variance on the dependent variable of percent change in depression score because it uses a 2×3 between subject quasi variable design. A Plagiarized Student Sample: ORDER YOUR PAPER NOW


Andreassen, C.S., Billieux, J., Griffiths, M.D., Kuss, D.J., Demetrovics, Z., Mazzoni, E., Pallesen, S. (2016) The relationship between addictive use of social media and video games and symptoms of psychiatric disorders: A large-scale cross-sectional study. Psychology of Addictive Behaviors, 30 (2), 252-262

Cavozos-Rehg, P.A., Krauss, M.J., Sowles, S., Connolly, S., Rosas, C., Bhardwaj, M., Bierut, L.J. (2016) A content analysis of depression-related tweets. Computers in Human Behavior, 54, 351-357

Davies, B., Burrows, G., Poynton, C., (1975) A comparative study of four depression rating scales. Australian and New Zealand Journal of Psychiatry, 9, 21

Fergie, G., Hunt, K., Hilton, S., (2016) Social media as a space for support: Young adults’ perspectives on producing and consuming user-generated content about diabetes and mental health. Social Science and Medicine, 170, 46-54

Lee-Won, R.J., Herzog, L., Park Sung, G., (2015) Hooked on Facebook: The Role of Social Anxiety and Need for Social Assurance in Problematic Use of Facebook. Cyberpsychology, Behavior, and Social Networking, 18, 10

Naslund, J.A., Aschbrenner, K.A., McHugo, G.J., Jurgen, Marsch, L.A., Bartels, S.J., (2017) Exploring opportunities to support mental health using social media: A survey of social media users with mental illness. Early Intervention in Psychology

Pantic, I., Damjanovic, A., Todorovic, J., Topalovic, D., Bojovic-Jovic, D., Ristic, S., Pantic, S. (2012) Association between online social networking and depression in high school students: Behavioral physiology viewpoint. Psychiatria Danubina, 24 (1), 90-93

Perloff, R.M. (2014) Social Media Effects on Young Women’s Body Image Concerns: Theoretical Perspectives and an Agenda for Research. Sex Roles, 71 (11-12), 363-377

Reyes, A., Rosso, P., Buscaldi, D. (2012) From humor recognition to irony detection: The figurative language of social media. Data and Knowledge Engineering, 74, 1-12


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