Myryll Esco (myrealesco99@gmail.com)
Kenneth Llanasas (skycarlet13@gmail.com)
Vida Zoe Salino (vidazoesalino@gmail.com)
Carmel Guazon (carmelgrace.guazon@spus.edu.ph)
Lucy L. Teves, PhD, RN (Orcid No. 0000-0003-0939-2824)
ABSTRACT
The primary purpose of the study is to determine whether other people's attitudes and memes expressing depression influence how users perceive depressed people. The descriptive-correlational study approach was used to emphasize the degree of attitude toward depression and the frequency of interaction with memes that express depression among three hundred eighty-five random participants residing in Surigao del Norte. The statistical analysis tools that were used in treating the data were frequency count, mean, standard deviation, Pearson Correlation, and One-way ANOVA. Females between the ages of twenty and twenty-four comprise the majority of those who interact with memes that express depression and majority of the participants oftentimes interact with memes expressing depression. Results showed that there is a significant relationship between the participants’ attitude towards depression and their frequency of interaction with memes that express depression. When it comes to memes expressing depression, individuals are more likely to take them seriously than humorously. Moreover, results also showed that there is a significant difference between perceived attitude towards depression when grouped by sex. Furthermore, the null hypothesis that states there is no significant difference between the participants’ attitude towards depression and their frequency of interaction with memes that express depression when grouped according to their profile: Age, was rejected. As a result, it's critical to encourage Facebook users to exercise caution while interacting with memes that express depression.
Keywords: Attitude, Facebook Users, Memes, Depression
INTRODUCTION
According to Rogers (2021), memes are a spread of reproduced copies of various good and bad informative images with written texts mostly seen on the internet. Memes could also mean cultural composites (ideas, symbols, and behaviors) that circulated through replication and appropriation in various forms (Silvestri, 2014). Although it is usually based in popular culture, massively popularized, and shared via social media, blogs, email, and other avenues nowadays, it can also be thoughts, fragments of ideas, sounds, melodies, and various types of information (Amir, 2021). Some people nowadays develop and share more interactive memes on social media that cover a variety of topics. This can be used to convey a viewpoint on a societal norm, expose a social taboo, push a political agenda, reveal unspoken personal problems, inspire others to speak up, and, most significantly, to laugh about something (Amir, 2021). As the meme culture grows, it becomes evident that some memes’ topic is expressed more on mental health concerns. Specific messages may impact people's attitudes, beliefs, and behaviors regarding mental health issues (Lebowitz and Ahn, 2014).
Depression, specifically, is a common and severe mental health condition that has a negative effect on how you feel, think, and behave (American Psychology Association, 2021), and it has been observed on a topic or subject of internet memes posted on social media. These internet memes related to depression has potential to improve the mood of depressed people in terms of humor, relatability and shareability. (Akram, et. al, 2020). In addition, memes related to mental illness are utilized to disguise symptoms and serve as a defense mechanism for individuals who have experienced depression or anxiety. (Kariko and Anasih, 2019). Because it is difficult to engage with others face-to-face, depressed or anxious people use social media to ease the symptoms (Andreassen et al., 2016). As a result, memes are used in a variety of ways to send a message about depression that does not always correspond to a consistent meaning (Chateau, 2020). With that, the internet memes can influence the perception and interpretation of public viewers about the online messages in a variety of context-of which oftentimes, internet memes are generally perceived as more likable yet less pressing in conveying messages (Hecker, 2020).
The strength of one's attitude influences one's actions significantly. Simply saying, the more powerful ones’ attitude is, the more probable it is to impact a person’s actions (McLeod, 2018). In this context, the researchers postulate that other people’s attitudes conveyed on a meme about depression can impact viewers’ perceptions of depressed people. Thus, the aim of this study is to determine the perceived attitudes of Facebook users towards memes about depression. Once determined, provision and strengthening of public awareness is needed in order to understand the interaction on digital replication of expressive memes about depression, as well as its impact on viewers' perceptions of depressed individuals.
Conceptual Framework
This study adopts the ABC (Affect, Behavioral, Cognitive) model of attitude. Accordingly, an individual's feelings towards an attitude item are referred to as affective, and the response of a person towards an attitude object is referred to as behavior, while the term cognitive refers to an individual's beliefs/perception about an attitude entity (Jain, 2014).
The box contained the profile of the participants, which asked about their sex and age. Age refers to the number of years people have lived, and only between 18-34 years old is applicable in this study. According to statistics, Filipinos aged between 18-34 year old were the largest group of Facebook users in the Philippines, (NapoleonCat, 2020; Statista, 2021). Sex refers to their biological sex of being male or female. Many researchers have argued that sociodemographic factors influence the response to and the identification, distribution, and assessment of mental illness (Corrigan & Watson, 2007; Von dem Knesebeck, Angermeyer, et al., 2013; Park, Kim, Cho, & Lee, 2015). Thus, these factors help shape the individual's beliefs and attitudes.
The role of each component on ABC Model of attitude thus determines the association between the participants' frequency of engagement with memes that convey depression and their attitude toward depression. Affective refers to a person's feelings and ideas about something depending on their attitude (McLeod, 2018). In this study, the affective context delves into the expressive illustration of memes about depression. These memes are provided as examples in this study’s research questionnaire to help the participants internalize their feelings and thoughts on a receptive expression of memes about depression. Meanwhile, behavioural and cognitive contexts are being determined through the personal and perceived attitude towards depression, as well as the participants’ and their frequency of interaction with memes that express depression. The behavioural component of this study relates to the participants' intents, or their reactions (whether they regard depression memes as comical or serious) after being exposed to it. Thus, it is important to regard that one’s attitude has an impact on how a person will act or behave (McLeod, 2018).
On other hand, an individual's attitude towards an item cannot be established merely by identifying the ideas because emotion and the cognitive process regarding an attitude object operate together (Jain, 2014). The cognitive aspect holds an appraisal of the substance that serves as the foundation for a person's personal and perceived attitude toward depression (Jain, 2014). External influences have an impact on one’s attitudes and beliefs, but they also can influence internal effects. Attitudes and ideas, like actions, are impacted by external events, but they may also be influenced purposefully by one's free will (McLeod, 2018).
Demographic Profile: •Sex •Age |
Frequency to Interact with Memes about Depression context: Ø Humorous Ø Serious
|
Attitude towards depression in terms of: Ø Personal Ø Perceived
|
Recommendations
|
Figure 1. Schematic Diagram of the Study
METHOD
This study is a quantitative research using a descriptive-correlational design. To describe relationships between variables and demographics, researchers utilized a descriptive correlational research design (Al-Ghabeesh & Mahmoud, 2021). Quantitative research techniques entail quantifying and analyzing variables in order to acquire results. It comprises the use of certain statistical methodologies to the analysis of numerical data in order to answer questions. Descriptive-correlational design is also utilized to describe the relationship between variables (Apuke, 2017). Moreover, it helps determine the relationship between perceived attitudes and memes expressing depression.
Participants
The study considered asking participants’ personal and perceived attitude toward depression and their frequency participants' interaction with memes expressing depression. The researchers took several considerations in recruiting participants for this research study. According to the Philippine Statistics Authority (2020), the total population of Surigao del Norte is 534,686, which the researchers used to calculate the sample size of 385 participants using Cochran's sample size calculation for a limited population. Moreover, participants should only have a single account on Facebook that they are actively using. Simple random sampling was used to select the participants. Each person of the population has an equal probability of getting chosen as a participant in this approach. When deriving conclusions from the findings of a study, an unbiased random sample and a representative sample are essential. Because of the representativeness of a sample acquired by simple random sampling, it is acceptable to generalize the sample's conclusions back to the population (Sharma, 2017)
Research Instrument
The researchers use a survey questionnaire and informed consent that are easy to read and include clear instructions written with simple English. The questionnaire consists of a standardized test—Depression Stigma Scale (DSS) by Kathleen Griffiths, Ph.D., and questions made by the researchers. It is divided into three sections. The Part 1.allows for a better understanding of the participants' profiles, particularly age and sex. The researchers create the second section of the survey questionnaire, and it consists of 3 sub-parts.
Part 2.1 Meme Selection Criteria
A summary of the key meme selection criteria, organized by stages of memes replication and general selection criteria for memes.
Stages/Selectors | Objective | Subjective | Inter-subjective | Meme-centered |
Assimilation | distinctiveness | novelty simplicity coherence | authority formality | self-justification |
Retention | invariance controllability | coherence utility | conformity | self-reinforcement intolerance |
Expression | expressivity |
proselytism | ||
Transmission | publicity |
proselytism |
Note. Adapted from What makes a meme successful? Selection criteria for cultural evolution Book, by Heylighen, F. (1998).
Mostly, the host disseminate internet memes in expressive context (Blackmore, 2000; Miller, 2017). Internet memes are popular in form of texts and image which is usually interpreted in a variety of ways by readers (Sanchez, 2020). It is necessary on comprehending online memes that requires a focus on human agency on understanding the information to be most likely influential to others (Shifman 2013a; Miltner, 2017). Thus, researchers focused the criteria on assimilation stage identify the theme, self-loathing and worthlessness that is emphasized to any stages criteria such objective, subjective, inter subjective and meme-centered.
In assimilation stage, the object criteria denote distinctiveness in terms of details or contrasting facts are more likely to be recognized and understood that the memes is about depression when it presents a context of worthlessness and self-loathing as one of the classifications in DSM-5. In subjective criteria, the memes about depression is a form of novelty, it is when the person who assimilates the meme about depression can make viewers’ perceived a context of worthlessness and self-loathing. In the inter-subject criteria, the memes about depression propagate with one another based on formality, the host is at least express the original memetic substances of memes about depression to encourage the viewers to interact on the topic. In the meme-centered criteria, the self-justification is when viewers will understand and accept the host thoughts of worthlessness and self-loathing presented on a meme. Internet memes work because of their ‘emotional resonance’, accordingly internet memes enact mimetic gains a response for being humorous (Piata, 2018), but some depressed people use social media to ease the symptoms and being depressed is not funny (suicide is not), so it should not be mocked (Chateau, 2020: Andreassen et al., 2016).
The images below are examples of a memes that has a theme of self-loathing and worthlessness as defined on DSM-5 about depression. Also, the researchers decided to show the 4 memes based on using keywords such as ‘depression meme’ ‘humorous depression meme’, ‘serious depression meme’ when searching. The four pictures were also based on underlying factos such as their predisposition to convey valance and arousal, as well as the presence of affective content relevant to the experience of depression (e.g., death, suicide, isolation, hopelessness, depressed mood, manic symptoms), as outlined in the DSM-5 criteria for Major Depressive Disorder (Regier, D., Kuhl, E., and Kupfer D., 2013). This criteria in choosing is in accordance to what Akram, et. al (2020) did for their study “Exploratory study on the role of emotion regulation in perceived valence, humour, and beneficial use of depressive internet memes in depression”.
Image 1. Serious Memes About self-loathing on assimilation stage.
The context of Memes posted by S.J. Foster is kind of self-loathing saying “ I’m always depressed” and “Me making fun of my own suffering so it doesn’t look that serious”, a genuine message is detailed about depression that make a memes distinct in assimilation stage. In terms of subjective and inter subject criteria, a person named E. Martinez commented/participated “I feel ya brother”, it means the public views understand what the message convey. Thus, meme-centered criteria is successfully meet the assimilation stage to make it clear for viewers of what the memes meant for.
Image 2. Humorous about self-loathing on assimilation stage.
The context of memes posted on depression memes page is about self-loathing saying “When I’m deep in a depressive episode and someone tells me to ‘stay positive’.”, a mimic message tell about the personal experience on depression that make a memes distinct in assimilation stage. In terms of subjective and inter subjective criteria, many people participated on the comment section and consider the meme as relatable. Participants have mixed reaction response based on emoticons used such as laughing, loved, like, care, angry and sad. Thus, focusing on meme-centered criteria it successfully achieved the viewer’s understand the meaning of meme.
Image 3. Serious Memes About self-loathing on assimilation stage.
The context of memes posted by Hero Kun saying “Trust me, depression is not a joke”, a genuine message about depression that shared topic that maybe based on personal experience that makes a memes belong with the other depression memes. In terms of subjective and inter subject, the viewer’s perhaps watched the meme but nobody participated. Thus, the meme-centered criteria was achieved because of viewers and its content about depression.
Picture 1: |
Picture 2. |
Image 4. Humorous memes about worthlessness in assimilation stage.
The context of memes posted on Ludicrous.glow is about depression, the person who shared the meme emphasized that “depression is not an excuse! It’s serious”. It is mimic because the meme tells that people used depression as excuse that somehow discard having mental illness. In terms of subjective and inter subject, the viewer’s perhaps watched the meme but nobody participated. Thus, the meme-centered criteria was achieved because of viewers and its content about depression.
Finally, the Part 3 is based on a standardized test: Depression Stigma Scale (DSS) by Kathleen Griffiths, Ph.D., an 18 item 5-point Likert scale. The researchers decided to use the following questions from the standardized test: Under Personal Stigma Subscale – “People with depression could snap out of it if they wanted.”, “Depression is a sign of personal weakness.”, “Depression is not a real medical illness.”, “People with depression are dangerous.”, “It is best to avoid people with depression so that you don’t become depressed yourself.”, “People with depression are unpredictable.”, “If I had depression, I would not tell anyone.”, “I would not employ someone if I knew they had been depressed.”, and “I would not vote for a politician if I knew they had been depressed.”; Under Perceived Stigma Subscale – “Most people believe that people with depression could snap out of it if they wanted.”, “Most people believe that depression is a sign of personal weakness.”, “Most people believe that depression is not a real medical illness.”, “Most people believe that people with depression are dangerous.”, “Most people believe that it is best to avoid people with depression so that you don’t become depressed yourself.”, “Most people believe that people with depression are unpredictable.”, “If they had depression, most people would not tell anyone.”, “Most people would not employ someone they knew had been depressed.” and “Most people would not vote for a politician they knew had been depressed.”.
According to Griffiths, The Personal Stigma Subscale assesses stigma in participants' attitude toward depression by asking them to rate their agreement with nine statements about depression. While The Perceived Stigma Subscaleassesses a respondent's perception of others' attitudes about depression by asking them to indicate what they feel the majority of other people believe about nine statements. Each item is scored on a five-point Likert scale, one representing "strongly disagree" and five representing "strongly agree." Each subscale's total score varied from 0 to 36, with higher values indicating more significant stigma. The DSS has been shown to have sufficient internal consistency and test-retest reliability.
DSS Reliability result. According to Griffiths’ norms and psychometric properties, Test-retest reliability is r = 0.71 (n=435) (DSS- Personal Stigma Subscale) and r = 0.67 (n =434) (DSS- Perceived Stigma Subscale). With a sample of 487, Internal consistency is α = 0.75 (DSS- Personal Stigma Subscale), α = 0.75 (DSS- Perceived Stigma Subscale), and α = 0.78 (Total Depression Stigma Scale).
Pilot Test. The data gathered to 15 samples following three ethical principles and guidelines according to Belmont Report: Principle of beneficence, principle of human dignity and the principle of justice
Instrument | α | Remark | |
Part 2 | How frequently do you laugh to memes about depression? | 8.67 | Highly reliable |
How frequently do you take serious of memes about depression? | 8.93 | Highly reliable | |
How frequently do you interact with memes about depression generally? | 9.12 | Highly reliable | |
Part 3.1 | Personal (9 indicators) | 8.97 | Highly reliable |
Part 3.2 | Perceived (9 indicators) | 8.74 | Highly reliable |
Figure 3. PILOT TEST RESULT
Construct | Indicators | Scale Mean if Item Deleted | Scale Variance if Item Deleted | Corrected Item-Total Correlation | Cronbach’s Alpha (α) if Item Deleted | Remark (Measure) |
Personal | B1 | 20.1333 | 12.267 | 0.183 | 0.644 | Good |
B2 | 20.4667 | 11.124 | 0.453 | 0.583 | Good | |
B3 | 21.3333 | 11.667 | 0.164 | 0.663 | Good | |
B4 | 20.5333 | 9.552 | 0.59 | 0.533 | Good | |
B5 | 21.5333 | 10.981 | 0.537 | 0.568 | Good | |
B6 | 20.5333 | 12.267 | 0.328 | 0.615 | Good | |
B7 | 21.6667 | 14.095 | - 0.123 | 0.703 | Good | |
B8 | 20.6667 | 10.81 | 0.505 | 0.57 | Good | |
B9 | 20.8667 | 11.124 | 0.393 | 0.595 | Good | |
Perceived | C1 | 22.2667 | 11.067 | 0.215 | 0.68 | Good |
C2 | 22.2667 | 9.352 | 0.58 | 0.577 | Good | |
C3 | 22.6 | 12.971 | - 0.044 | 0.731 | Good | |
C4 | 22.2 | 10.457 | 0.465 | 0.613 | Good | |
C5 | 22.9333 | 10.781 | 0.382 | 0.632 | Good | |
C6 | 22.4667 | 11.552 | 0.427 | 0.631 | Good | |
C7 | 22.3333 | 12.238 | 0.327 | 0.65 | Good | |
C8 | 22.3333 | 10.552 | 0.535 | 0.602 | Good | |
C9 | 22.3333 | 10.981 | 0.433 | 0.623 | Good | |
Figure 4. INTER-ITEM STATISTICS
Data Analysis
Mean and Standard Deviation. Mean refers to the average scores collected from the raw data. The standard deviation is a statistic that calculates the square root of the variance and measures the dispersion of a dataset relatively to its mean. These statistical tools describe the average and spread of data on the following statement of the problems: What is the participants’ frequency of interaction with memes that express depression context (Humorous and Serious)?, What is the participants’ frequency of interaction with memes that express depression in general?, and What is the participants’ attitude towards depression (Personal and Perceived)?.
Pearson Correlation. The test statistic Pearson's correlation coefficient assesses the statistical relationship, or association, between two continuous variables. It provides information on the magnitude and direction of the relationship's association, or correlation. This statistical tool measures the first null hypothesis regarding the significant relationship between the participants’ attitude towards depression and their frequency of interaction with memes that express depression.
One-way ANOVA. In testing the second null hypothesis regarding the significant difference between the participants’ attitude towards depression and their frequency of interaction with memes that express depression when grouped according to their profile variable, a One-way Analysis of Variance (ANOVA) was conducted. This was used to analyze if significant differences exist between the respective mean scores of groups. A 95% confidence interval was set. After which, the first step was to determine if the ANOVA result has a significant F test, in this case, any value lesser than (α=0.05) is deemed statistically significant.
RESULTS AND DISCUSSION
This chapter explains the study's data analysis and findings. The following data can be found: a demographic profile (both sexes and ages); frequency of interaction in memes that both express humorous and serious depression, as well as general interaction on depression memes; personal and perceived attitudes toward depression. Moreover, it discusses the relationship and difference of the null hypotheses of the study: Is there a significant relationship between the participants’ attitude towards depression and their frequency of interaction with memes that express depression? and When grouped according to their profile variable, is there a significant difference between the participants’ attitude towards depression and their frequency of interaction with memes that express depression?.
Demographic Profile
Table 1. Profile of the Respondents
Profile Variables | f (n=385) | % |
Sex | ||
Male | 169 | 43.90 |
Female | 216 | 56.10 |
Age | ||
15-19 years old | 58 | 15.06 |
20-24 years old | 217 | 56.36 |
25-29 years old | 104 | 27.01 |
30-34 years old | 6 | 1.56 |
One of the most core characteristics in determining the profile of the participants is their sex and age. In terms of sex, the majority of the participants were female, totalling in 216 (56.10 %), while 169 (43.90 %) were men out of 385 participants. This is in line with the aggregate statistics data from August 2021, which showed that the Philippines had 93 million Facebook users, the majority of whom were women (54%) (NapoleonCat, 2020; Statista, 2021).
Furthermore with a total population of 385, results showed that participants belong to ages 15-19 years old were 58 (15.06%), then 20-24 years old with 217 (56.36%), 25-29 years old with 104 (27.01%), and 30-34 years old with 6 (1.56%). In the Philippines, 31% of Facebook users are between the ages of 18 and 24 followed by 29% between the ages of 25-34 as of August 2021 which is in line with the results of the study (Statista, 2021).
Participants’ frequency of interaction with memes that express depression.
Table 2. frequency of interaction with memes that express depression.
Indicators | x̄ | σ | Interpretation |
How frequently do you laugh to memes about depression? | 2.24 | 0.95 | Rarely |
How frequently do you seriously look at memes about depression? | 2.77 | 0.78 | Oftentimes |
How frequently do you interact with memes about depression generally? | 2.51 | 0.78 | Oftentimes |
Average | 2.51 | 0.83 | Oftentimes |
Scale | Range | Interpretation |
4 | 3.25-4.00 | All the time |
3 | 2.50-3.24 | Oftentimes |
2 | 1.75-2.49 | Rarely |
1 | 1.00-1.74 | Not at all |
Legend:
Results in table 2 shows that majority of the participants answered oftentimes on how they frequently interact with memes about depression in general (X̄=2.51, σ =0.78). Memes have a significant impact on young people although the emerging visual culture may alter their perceptions of visual representation through digital media (Han, 2020). Viewers may perceive a variety of interpretations from a hilarious meme and the more popular the content becomes, the more interpretations there are; yet, interpretations are contingent on a culture's ability to be more open-minded (Laineste & Voolaid, 2016). Although, it is also worth mentioning that the participants in this study oftentimes prefer interacting on memes expressing depression seriously (X̄=2.77, σ =0.78) rather than humorously (X̄=2.24, σ =0.95).
Participants’ attitude towards depression in terms of personal and perceived.
Table 3 participants’ attitude towards depression in term if personal and perceived.
Indicators | X̄ | σ | Interpretation |
Personal | |||
People with depression could snap out of it if they wanted. | 2.76 | 0.86 | Agree |
Depression is a sign of personal weakness | 2.14 | 0.89 | Disagree |
Depression is not a real medical illness | 1.66 | 0.71 | Strongly disagree |
People with depression are dangerous. | 2.59 | 0.92 | Agree |
It is best to avoid people with depression so that you don’t become depressed yourself. | 2.01 | 0.87 | Disagree |
People with depression are unpredictable. | 3.06 | 0.68 | Agree |
If I had depression I would not tell anyone. | 2.26 | 0.84 | Disagree |
I would not employ someone if I knew they had been depressed. | 2.19 | 0.79 | Disagree |
I would not vote for a politician if I knew they had been depressed. | 2.75 | 0.87 | Agree |
Average | 2.38 | 0.83 | Disagree |
Perceived | |||
Most people believe that people with depression could snap out of it if they wanted. | 3.01 | 0.66 | Agree |
Most people believe that depression is a sign of personal weakness. | 2.56 | 0.89 | Agree |
Most people believe that depression is not a real medical illness. | 2.25 | 0.87 | Disagree |
Most people believe that people with depression are dangerous. | 2.83 | 0.75 | Agree |
Most people believe that it is best to avoid people with depression so that you don’t become depressed yourself. | 2.42 | 0.84 | Disagree |
Most people believe that people with depression are unpredictable. | 3.07 | 0.55 | Agree |
If they had depression, most people would not tell anyone. | 2.76 | 0.81 | Agree |
Most people would not employ someone they knew had been depressed. | 2.53 | 0.76 | Agree |
Most people would not vote for a politician they knew had been depressed. | 2.95 | 0.74 | Agree |
Average | 2.71 | 0.76 | Agree |
Legend:
Scale | Range | Interpretation |
4 | 3.25-4.00 | Strongly Agree (SA) |
3 | 2.50-3.24 | Agree (A) |
2 | 1.75-2.49 | Disagree (D) |
1 | 1.00-1.74 | Strongly Disagree (SD) |
Meanwhile table 3 presents the participants’ attitude towards depression in terms of personal and perceived. The majority of participants disagreed (X̄=2.38, σ =0.83) with the idea of having a personal negative attitude toward depressed people. While, majority of the participants agreed that most people around them (perceived) would have a negative attitude towards depressed people (X̄=2.71, σ =0.76). These findings are consistent with prior research, which found that perceived stigma was consistently higher than personal stigma in the general population (Reavley & Jorm, 2011); Coppens, 2013; Yang et al., 2020).
Moreover, mental illnesses are widely ignored or feared by Asians, according to a prior research (Ng, 1997; Yang et al., 2020). This notion can be supported in statements People with depression could snap out of it if they wanted(X̄=2.76, σ =0.86), People with depression are dangerous (X̄=2.38, σ =0.83), People with depression are unpredictable(X̄=3.06, σ =0.68), and I would not vote for a politician if I knew they had been depressed (X̄=2.75, σ =0.87) which most participants agreed on the said statements. On the other hand, most participants agreed that most people around them would believe the following statements Most people believe that people with depression could snap out of it if they wanted (X̄=3.01, σ =0.66), Most people believe that depression is a sign of personal weakness (X̄=2.56, σ =0.89), Most people believe that people with depression are dangerous (X̄=2.83, σ =0.75), Most people believe that people with depression are unpredictable (X̄=3.07, σ =0.55), If they had depression, most people would not tell anyone (X̄=2.76, σ=0.81), Most people would not employ someone they knew had been depressed (X̄=2.53, σ =0.76), and Most people would not vote for a politician they knew had been depressed (X̄=2.95, σ =0.74). Although negative perception in mental health is still a worldwide issue, there is evidence that the sociocultural context (collective and individual values, aspirations, norms, and social expectations) can influence how negative perception is exhibited and the intensity of reputation in various social groups (Yang et al., 2007, 2013; Lien et al., 2015; Chang et al., 2016; Mascayano et al., 2020). One thing to emphasize also is that most of the participants strongly disagree on the statement Depression is not a real medical illnesswhich can be supported by a 2018 survey that resulted with 78% participants believing that depression is considered a mental illness (APA, 2019).
Significant relationship between the participants’ attitude towards depression and their frequency of interaction with memes that express depression.
Table 4. The significant relationship between the participants’ attitude towards depression and their frequency of interaction with memes that express depression
Interaction | Attitude | p-value | Decision |
Frequency of interaction | Personal | 0.0086 | Reject Ho |
Perceived | 0.0143 | Reject Ho |
As presented in the table, there is a significant relationship between the participants’ attitude towards depression and their frequency of interaction with memes that express depression. The study’s findings resulted in rejecting the null hypothesis between the participants’ attitude (both personal and perceived) towards depression and their frequency of interaction with memes that express depression. The computed p-values between personal attitude and memes expressing depression (p = 0.0086) and perceived attitude and memes expressing depression (p = 0.0143) are both less than the 0.05 significance level. This supports with how internet memes can influence public viewers' perception and understanding of internet activities in a number of contexts (Hecker, 2020).
Significant difference between the participants’ attitude towards depression and their frequency of interaction with memes that express depression.
Table 5. The significant difference between the participants’ attitude towards depression and their frequency of interaction with memes that express depression.
Profile Variables | Dependent Variables | p-value | Decision |
Sex | Interaction | 0.2466 | Do not reject Ho |
Personal | 0.4054 | Do not reject Ho | |
Perceived | 0.0259 | Reject Ho | |
|
|
|
|
Age | Interaction | 0.0129 | Reject Ho |
Personal | 0.0000 | Reject Ho | |
Perceived | 0.0283 | Reject Ho |
The results, as shown in the table, revealed a variety of outcomes. A one-way ANOVA revealed that there was a significant difference in perceived attitude towards depression between sexes (p= 0.0259 < α = 0.05). This finding is supported by one study from Chile and Colombia which there was a significant difference on gender in the DSS-Personal scores, with women scoring lower than males (Martinez et. al, 2020). However, there’s no significant difference between sexes and the following problems: personal attitude towards depression (p = 0.4054 > α = 0.05) and frequency of interaction with memes that express depression (p = 0.2466 > α = 0.05). These findings are consistent with one study’s findings that found no gender differences on the DSS-Personal subscale in a sample of Arab teenagers (Dardas et al., 2017) and another study results that stated there was no significant difference in meme awareness based on an individual's sex (Laranjo, 2020).
On the other hand, a one-way ANOVA revealed that there was a significant difference when grouped by age between the following problems: perceived attitude towards depression (F= 3.06, p = 0.0283 < α = 0.05), personal attitude towards depression (F= 11.33, p = 0.0000 < α = 0.05), and frequency of interaction with memes that express depression (F= 3.64, p = 0.0129 < α = 0.05). According to prior studies, there may be generational differences in mental health knowledge because younger participants may have a better level of mental health awareness and hence support less negative perceptions about depression than middle-aged or elderly individuals (Griffiths& Jorm, 2008; Reavley et al., 2014; Yang et al, 2020).
Findings
The findings of the study are revealed as follows:
1. In terms of the profile demographics, 56% of the 385 participants are female, while 44% are male. As per the results, the majority of those who responded to the study survey are females. In terms of age, 385 participants are 56% of the age 20-24 years old, followed by 27% of the age 25-29 years, then 15% of the age 15-19 years old, and lastly 1.56% of the age 30-34 years old. Majority of the participants’ ages were 20-24 years old.
2. The participants rarely interact with memes that express depression through humorous context (X̄=2.24, σ =0.95). While, the participants oftentimes interact with memes that express depression through serious context (X̄=2.77, σ=0.78). In general, majority of the participants oftentimes interact with memes expressing depression (X̄=2.51, σ=0.78).
3. The majority of participants disagree (X̄=2.38, σ =0.83) with the idea of having a personal negative attitude toward depressed people. While, majority of the participants agree that the most people around them (perceived) would have a negative attitude towards depressed people (X̄=2.71, σ =0.76).
4. The study’s findings resulted in rejecting the null hypothesis between the participants’ attitudes both personal (p = 0.0086 < α = 0.05) and perceived (p = 0.0143 < α = 0.05) towards depression and their frequency of interaction with memes that express depression. This suggests that there is a significant relationship between the participants’ attitude towards depression and their frequency of interaction with memes that express depression.
5. When being grouped by sex, a one-way ANOVA revealed that there was a statistically significant difference in perceived attitude towards depression (p= 0.0259 < α = 0.05) which would reject the null hypothesis. Although,there’s no significant difference in the following problems: personal attitude towards depression (p = 0.4054 > α = 0.05) and frequency of interaction with memes that express depression (p = 0.2466 > α = 0.05) between sexes. The two latter results in not rejecting the null hypothesis.
6. When being grouped by age, a one-way ANOVA revealed that there was a significant difference between the following problems: perceived attitude towards depression (p = 0.0283 < α = 0.05), personal attitude towards depression (p = 0.0000 < α = 0.05), and frequency of interaction with memes that express depression (p = 0.0129 < α= 0.05). Thus fully rejecting the null hypothesis that states there is no significant difference between the participants’ attitude towards depression and their frequency of interaction with memes that express depression when grouped according to their profile: Age.
Conclusions
Based on the indicated findings, the following conclusions were drawn:
1. The majority of the Facebook users who participated are within the age of 20-24 years old and are primarily female.
2. When it comes to memes expressing depression, people would interact with it seriously rather than humorously. This suggests that specific values are involved when interacting with memes expressing depression.
3. Although the participants are personally aware of the concept of depression, the people around them do not. This implies that the general population of Surigao del Norte still has insufficient understanding of depression as a mental illness.
4. Facebook users' perceptions and understanding of depression and depressed people can be influenced by internet memes whether if it is humorous or serious.
5. In general, memes that express depression are interacted with both sexes at all ages. This supports the idea that memes are for everyone, regardless of their context. Thus, it is important to remind Facebook users to tread carefully while engaging with memes.
Recommendations
The results of our study were indicated by the cumulative analysis of the questionnaire responses. As a result, the researchers made the following suggestions:
1. For Facebook users, the researchers want to emphasize that there’s a wide range of content available on the internet. Whatever content they interact with should not directly imply a poor opinion about another person.
2. For Content Creators, there should be accountability when publishing materials (such as memes) that deal with themes such as depression while making content. Creating materials with topics like depression should be taken seriously and be based on facts.
3. For Psychology Clubs and other Organizations, hosting programs aimed at the general public regarding Depression as a mental illness can help improve its perception and bring more awareness about it.
4. For Guidance Counselors, assisting students in being more conscious of their attitudes and behaviors may help lessen negative perceptions about mental illness. This could be conducted by one-on-one counseling or holding a seminar/ workshop about the pros and cons of Facebook content such as memes expressing depression.
5. For future researchers who will utilize this work as a basis for their related studies, specific genders (such as transgender, non-binary, etc.) could be tested for significant relationships on levels of attitude towards depression.
6. More research must be done on the survey questionnaires (especially parts 1 and 2), and if possible, find a standardized test pertaining to memes that can be utilized.
7. In terms of sample size, it's better to keep it within the researchers' university first so that the findings may directly benefit the school.
8. It is also suggested that this study can be conducted as Qualitative Research.
9. Investigating factors such as religion and cultural differences influencing the attitude of participants regarding depression is encouraged.
References:
· Addis, M. E., & Hoffman, E. (2017). Men’s depression and help-seeking through the lenses of gender. The Psychology of Men and Masculinities., 171–196. https://doi.org/10.1037/0000023-007
· Akram, U., Drabble, J., Cau, G., Hershaw, F., Rajenthran, A., Lowe, M., Trommelen, C., & Ellis, J. G. (2020). Exploratory study on the role of emotion regulation in perceived valence, humour, and beneficial use of depressive internet memes in depression. Scientific Reports, 10(1). https://doi.org/10.1038/s41598-020-57953-4
· American Psychological Association. (2019, May). Survey: Americans becoming more open about mental health. https://www.apa.org/news/press/releases/2019/05/mental-health-survey
· American Psychology Association. (2021). What Is Depression? Web Starter Kit. https://www.psychiatry.org/patients-families/depression/what-is-depression
· Amir, M. (2021). What are Internet Memes and How They are Used for Different Purposes? SSRN Electronic Journal. Published. https://doi.org/10.2139/ssrn.3874090
· Andreassen C S, Griffiths M D, Demetrovics Z, Pallesen S, Billieux J, Kuss D and Mazzoni E (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 pp 252-62
· Apuke, O. (2017). Quantitative Research Methods : A Synopsis Approach. Arabian Journal of Business and Management Review (kuwait Chapter).. 6. 40-47. 10.12816/0040336.
· Beach, S. (2021). Big Mood: Ambient Suffering and Depression Memes [Doctoral dissertation, Ohio University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_nux̄=ohiou161899951719584
· Bensaber, Y. (2020). Humor and Gender: Males and Females’ Understanding and Appreciation of Humor in Memes and Written Jokes: A Case Study of 3rd Year Students of English at Frères Mentouri Constantine 1 University. SSRN Electronic Journal. Published. https://doi.org/10.2139/ssrn.3716928
· Brashier, N. M., & Marsh, E. J. (2020). Judging Truth. Annual Review of Psychology, 71(1), 499–515. https://doi.org/10.1146/annurev-psych-010419-050807
· Chapman, C. (2021, April 14). Media, Memes, and Mediocrity. Digital Commons@Georgia Southern. https://digitalcommons.georgiasouthern.edu/curio-symposium/2021/2021/9/
· Chateau, L. (2020). “Damn I Didn’t Know Y’all Was Sad? I Thought It Was Just Memes”: Irony, Memes, and Risk in Internet Depression Culture. M/C Journal, 23(3). https://doi.org/10.5204/mcj.1654
· Corrigan, P. W., Powell, K. J., Fokuo, J. K., & Kosyluk, K. A. (2014). Does humor influence the stigma of mental illnesses?.The Journal of nervous and mental disease, 202(5), 397–401. https://doi.org/10.1097/NMD.0000000000000138
· Cuevas, Eva Maria H. (2021). Meme sharing "culture" and psychological well-being from a neuroscientific framework. Facultad de Ciencias Sociales, UPR RP. https://www.researchgate.net/profile/Eva-Hernandez-Cuevas/publication/352258917_MEME_SHARING_CULTURE_AND_PSYCHOLOGICAL_WELL-BEING_FROM_A_NEUROSCIENTIFIC_FRAMEWORK
· Dardas, L. A., Silva, S. G., Smoski, M. J., Noonan, D., and Simmons, L. A. (2017). Personal and perceived depression stigma among Arab adolescents: associations with depression severity and personal characteristics. Arch. Psychiat. Nurs. 31, 499–506. doi: 10.1016/j.apnu.2017.06.005
· Depression (major depressive disorder) - Symptoms and causes. (2018, February 3). Mayo Clinic. https://www.mayoclinic.org/diseases-conditions/depression/symptoms-causes/syc-20356007
· Depression in women: Understanding the gender gap. (2019, January 29). Mayo Clinic. https://www.mayoclinic.org/diseases-conditions/depression/in-depth/depression/art-20047725
· Depression Memes. (17–09-13). Depression Memes [Photo]. https://m.facebook.com/DepressedFuks/photos/a.1565600657087399/1833401736973955/?type=3&source=48
· Devendorf, A., Bender, A., & Rottenberg, J. (2020). Depression presentations, stigma, and mental health literacy: A critical review and YouTube content analysis. Clinical Psychology Review, 78, 101843. https://doi.org/10.1016/j.cpr.2020.101843
· Duchscherer, K. M., & Dovidio, J. F. (2016). When memes are mean: Appraisals of and objections to stereotypic memes. Translational Issues in Psychological Science, 2(3), 335–345. https://doi.org/10.1037/tps0000080
· Dy, M.F & Maglunog, G.P. (2019). Facebook Usage and Depression Levels of Selected Filipino College Students. International Journal of Psychology and Educational Studies, 6(2), 35-50. doi.org/10.17220/ijpes.2019.02.004
· Faintukh E.D., Kholmogorova A. B. Communication About Depression on the Internet – factor of Psychopathology or Resource for Psychotherapy? // Sovrem. ter. psih. rasstrojstv [Current Therapy of Mental Disorders]. – 2021. – No. 1. – P. 44–54. – DOI: 10.21265/PSYPH.2021.39.62.006 [in Russian]. https://www.psypharma.ru/sites/default/files/2021-1-05-04-44-54.pdf
· Flecha Ortiz, J. A., Santos Corrada, M. A., Lopez, E., & Dones, V. (2020). Analysis of the use of memes as an exponent of collective coping during COVID-19 in Puerto Rico. Media International Australia, 178(1), 168–181. https://doi.org/10.1177/1329878x20966379
· Foster, S. J. (2020, October 8). Depression [Facebook status update]. Facebook. https://m.facebook.com/photo.php?fbid=974544896390093&id=100015037757335&set=a.126858907825367
· Gao, W., Ping, S., & Liu, X. (2020). Gender differences in depression, anxiety, and stress among college students: A longitudinal study from China. Journal of Affective Disorders, 263, 292–300. https://doi.org/10.1016/j.jad.2019.11.121
· Gomez-Baya, D., Mendoza, R., Paino, S., & de Matos, M. G. (2017). Perceived emotional intelligence as a predictor of depressive symptoms during mid-adolescence: A two-year longitudinal study on gender differences. Personality and Individual Differences, 104, 303–312. https://doi.org/10.1016/j.paid.2016.08.022
· Griffiths, K. (n.d.). Depression Stigma Scale (DSS) | Research School of Population Health. Research School of Population Health. Retrieved July 9, 2021, from https://rsph.anu.edu.au/research/tools-resources/depression-stigma-scale-dss
· Hakoköngäs, E., Halmesvaara, O., & Sakki, I. (2020). Persuasion Through Bitter Humor: Multimodal Discourse Analysis of Rhetoric in Internet Memes of Two Far-Right Groups in Finland. Social Media + Society, 6(2), 205630512092157. https://doi.org/10.1177/2056305120921575
· Han, Y. (2020). Design a Meme: Visual Representation, Creative Strategies and Memetic Culture. York Space Institutional Repository. Published. http://hdl.handle.net/10315/37721
· Hecker, Rabea (2020) Are you serious it is just a joke? : The influence of Internet memes on the perception and interpretation of online communication in social media.
· Hero Kun. (2021, October 9). Depression [Facebook status update]. Facebook. https://m.facebook.com/photo.php?fbid=133052498529863&id=100054753409310&set=pb.100054753409310.-2207520000..&source=42
· Heylighen, F. (1998). [PDF] What makes a meme successful? Selection criteria for cultural evolution | Semantic
· Scholar. Semanticscholar. https://www.semanticscholar.org/paper/What-makes-a-meme-successful-Selection-criteria-for-Heylighen/583018650aeb7c88627743162be28fd912e9e7f3
· Jain, V. (2014). 3D model of attitude. International Journal of Advanced Research in Management and Social Sciences, 3(3), 5–6. https://garph.co.uk/ijarmss/mar2014/1.pdf
· Jawali, A. (2018, April). Post Meme-llennials: A Descriptive Correlational Study on the Amount of Social Media Exposure and Impacts of Internet Memes on the Social Behavior of Post-Millennials. ResearchGate.https://www.researchgate.net/publication/337151592_Post_Meme-llennials_A_Descriptive_Correlational_Study_on_the_Amount_of_Social_Media_Exposure_and_Impacts_of_Internet_Memes_on_the_Social_Behavior_of_Post-Millennials
· Kariko, A. A. T., & Anasih, N. (2019a). Laughing at one’s self: A study of self-reflective internet memes. Journal of Physics: Conference Series, 1175, 012250. https://doi.org/10.1088/1742-6596/1175/1/012250
· Kemp, S. (2018, January 30). Digital in 2018: World’s internet users pass the 4 billion mark. We Are Social. https://wearesocial.com/blog/2018/01/global-digital-report-2018
· Klein, O. (2020, March 15). MISLEADING MEMES The effects of deceptive visuals of the British National Party. PArtecipazionee COnflitto https://www.google.com/url?sa=t&source=web&rct=j&url=https://cadmus.eui.eu/bitstream/handle/1814/67311/Misleading_memes.pdf%3Fsequence%3D1%26isAllowed%3Dy&ved=2ahUKEwiBqJ_4ksbvAhXNa94KHREoChgQFjAAegQIARAC&usg=AOvVaw3nNkM4ckTD8fdNIJHXwRUe
· Laineste, L., & Voolaid, P. (2017). Laughing across borders: Intertextuality of internet memes. The European Journal of Humour Research, 4(4), 26–49. https://doi.org/10.7592/ejhr2016.4.4.laineste
· Laranjo, J. (2020, March 6). A quantitative study: the attitudes of grade 12 steam students on memes with sensitive content. Academia. https://www.academia.edu/42157624/A_QUANTITATIVE_STUDY_THE_ATTITUDES_OF_GRADE_12_STEAM_STUDENTS_ON_MEMES_WITH_SENSITIVE_CONTENT
· Lebowitz, M. S., & Ahn, W. (2014). Effects of biological explanations for mental disorders on clinicians’ empathy. Proceedings of the National Academy of Sciences, USA, 111, 17786–17790. https://doi.org/10.1073/pnas.1414058111
· Lin, C. (2014, September 5). Lin. AAAI Publications, Second AAAI Conference on Human Computation and Crowdsourcing. https://www.aaai.org/ocs/index.php/HCOMP/HCOMP14/paper/view/8980
· Ludicrous.glow. (2020, June 17). Depression [Facebook status update]. Facebook.https://m.facebook.com/100830715016435/photos/a.100866628346177/101544044945102/?type=3&_rdc=1&_rdr
· Martey, R. M. (2017, September 14). Affect and effect of Internet memes: assessing perceptions and influence of online user-generated political discourse as media, The. MountainScholar. https://mountainscholar.org/handle/10217/183936
· MartÃnez, V., Crockett, M. A., Jiménez-Molina, L., Espinosa-Duque, H. D., Barrientos, E., & Ordóñez-Carrasco, J. L. (2020). Stigmatizing Beliefs and Attitudes to Depression in Adolescent School Students in Chile and Colombia. Frontiers in Psychology, 11. https://doi.org/10.3389/fpsyg.2020.577177
· Mascayano, F., Toso-Salman, J., Ruiz, B., Warman, K., Jofré, A., Alvarado, R., et al. (2015). What matters most: stigma towards severe mental disorders in Chile, a theory-driven, qualitative approach. Rev. Fac. Cien. Med. Univ. Nac. Cordoba. 72, 25–260.
· May, L. (2020, February 10). Positive Effects of Social Media on Your Teen. Family Orbit Blog. https://www.familyorbit.com/blog/the-positive-effects-of-social-media-on-your-teen/
· Mcleod, S. (2018). Attitudes and Behavior | Simply Psychology. SimplyPsychology. https://www.simplypsychology.org/attitudes.html
· Milter, Kate M. (2017) Internet Memes. BK-SAGE-BURGESS ET AL-170281-Chp22.indd 412. https://static1.squarespace.com/static/593b1c3a03596e06e97407b7/t/5daf401ba50ae018ef6f6b99/1571766305082/Miltner+-+Internet+Memes.pdf
· NapoleonCat. (2020, January 1). Facebook users in Philippines - January 2020. https://napoleoncat.com/stats/facebook-users-in-philippines/2020/01/
· Nearchou, F. A., Bird, N., Costello, A., Duggan, S., Gilroy, J., Long, R., McHugh, L., & Hennessy, E. (2018). Personal and perceived public mental-health stigma as predictors of help-seeking intentions in adolescents. Journal of Adolescence, 66, 83–90. https://doi.org/10.1016/j.adolescence.2018.05.003
· Neubaum, G., & Krämer, N. C. (2016). Monitoring the Opinion of the Crowd: Psychological Mechanisms Underlying Public Opinion Perceptions on Social Media. Media Psychology, 20(3), 502–531. https://doi.org/10.1080/15213269.2016.1211539
· P. (2020, February 15). Memes Say Something About Our Mental Health. Psychreg. https://www.psychreg.org/memes-mental-health/
· Philippine Statistics Authority. (2016). 2015 Census of Population Demographic and Socioeconomic Characteristics Surigao del Norte (Report No. 2). https://psa.gov.ph/sites/default/files/16_Surigao%20del%20Norte.pdf
· Piata, A. (2018). Online Humorous representations of the 2015 Greek national elections. [E-book]. In The Dynamics of Interactional Humor. (Vol. 7, pp. 257–282). John BenjaminsPublishing Company. https://books.google.com.ph/books?hl=en&lr=&id=-qZFDwAAQBAJ&oi=fnd&pg=PA257&dq=info:d04UMxDV-moJ:scholar.google.com/&ots=khusQUgKrZ&sig=pFEPMLXaCaYwrPE5-3lCcrHohnE&redir_esc=y#v=onepage&q&f=false
· Priego-Parra, B. A., Triana-Romero, A., Pinto-Gálvez, S. M., Ramos, C. D., Salas-Nolasco, O., Reyes, M. M., Ramos-de-la-Medina, A., & Remes-Troche, J. M. (2020). Anxiety, depression, attitudes, and internet addiction during the initial phase of the 2019 coronavirus disease (COVID-19) epidemic: A cross-sectional study in México. MedRxiv the Preprint Server for Health Science. Published. https://doi.org/10.1101/2020.05.10.20095844
· Regier DA, Kuhl EA, Kupfer DJ (2013). The DSM-5: Classification and criteria changes. World Psychiatry. 2013;12(2):92-98. doi:10.1002/wps.20050
· Rivin, D. R., & Shcherbakova, O. (2021). Understanding of comical texts in people with different types of attitudes towards humour: Understanding of comical texts in people with different types of attitudes towards humour: Evidence from Internet memes. The European Journal of Humour Research, 2, 112–131.
· Robinson, P., Turk, D., Jilka, S., & Cella, M. (2018). Measuring attitudes towards mental health using social media: investigating stigma and trivialisation. Social Psychiatry and Psychiatric Epidemiology, 54(1), 51–58. https://doi.org/10.1007/s00127-018-1571-5
· Rogers, K. (2021). meme | Definition, Meaning, History, & Facts. Encyclopedia Britannica. https://www.britannica.com/topic/meme
· Salk, R. H., Hyde, J. S., & Abramson, L. Y. (2017). Gender differences in depression in representative national samples: Meta-analyses of diagnoses and symptoms. Psychological Bulletin, 143(8), 783–822. https://doi.org/10.1037/bul0000102
· Sanchez, Barbara C. (2020) "Internet Memes and Desensitization," Pathways: A Journal of Humanistic and Social Inquiry: Vol. 1 : Iss. 2 , Article 5.
Available at: https://repository.upenn.edu/pathways_journal/vol1/iss2/5
· Sanchez, M. J. (2020, December 11). Social media usage in the Philippines - statistics & facts. Statista. https://www.statista.com/topics/6759/social-media-usage-in-the-philippines/#dossierContents__outerWrapper
· Schomerus, G., Stolzenburg, S., Freitag, S., Speerforck, S., Janowitz, D., Evans-Lacko, S., Muehlan, H., & Schmidt, S. (2018). Stigma as a barrier to recognizing personal mental illness and seeking help: a prospective study among untreated persons with mental illness. European Archives of Psychiatry and Clinical Neuroscience, 269(4), 469–479. https://doi.org/10.1007/s00406-018-0896-0
· Schröder, J., Berger, T., Meyer, B., Lutz, W., Hautzinger, M., Späth, C., Eichenberg, C., Klein, J. P., & Moritz, S. (2017). Attitudes Towards Internet Interventions Among Psychotherapists and Individuals with Mild to Moderate Depression Symptoms. Cognitive Therapy and Research, 41(5), 745–756. https://doi.org/10.1007/s10608-017-9850-0
· Sharma, G. (2017). Pros and cons of different sampling techniques. International journal of applied research, 3(7), 749-752.
· Silvestri, L. (2014). Memes in Digital Culture, by LimorSchifman. Popular Communication, 12(3), 198–200. https://doi.org/10.1080/15405702.2014.929378
· Statista. (2021, August 13). Number of Facebook users Philippines 2017–2026. https://www.statista.com/statistics/490455/number-of-philippines-facebook-users/
· Tilley, Shannon, "What Do You Meme? An Exploration of Internet Communication Through Memes" (2021). Senior Capstone Projects. 1066.
https://digitalwindow.vassar.edu/senior_capstone/1066
· Valkenburg, P. M. (2017). Understanding Self-Effects in Social Media. Human Communication Research, 43(4), 477–490. https://doi.org/10.1111/hcre.12113
· Wigmore, I. (2020, December 15). social media. WhatIs.Com. https://whatis.techtarget.com/definition/social-media
· Williams, A., Oliver, C., Aumer, K., & Meyers, C. (2016). Racial microaggressions and perceptions of Internet memes. Computers in Human Behavior, 63, 424–432. https://doi.org/10.1016/j.chb.2016.05.067
· Wong, E. F. (2021, January 8). Cognitive and motivational factors driving sharing of internet memes. Memory & Cognition. https://link.springer.com/article/10.3758/s13421-020-01134-1?error=cookies_not_supported&code=f2e6df21-77d2-43b3-82d2-a9d1599e2ac6
· Wong, T. Y., Yuen, K. S., & Li, W. O. (2015). A basic need theory approach to. Frontiers in Psychology, 1562.
· Yang, F., Yang, B. X., Stone, T. E., Wang, X. Q., Zhou, Y., Zhang, J., & Jiao, S. F. (2020). Stigma towards depression in a community-based sample in China. Comprehensive Psychiatry, 97, 152152. https://doi.org/10.1016/j.comppsych.2019.152152
· Yu, L., Jiang, W., Ren, Z., Xu, S., Zhang, L., & Hu, X. (2021). Detecting changes in attitudes toward depression on Chinese social media: A text analysis. Journal of Affective Disorders, 280, 354–363. https://doi.org/10.1016/j.jad.2020.11.040.
