Method
Procedures
The objective for this study was to examine how does social media reveal our anxiety disorders during the pandemic. This study conducted a content analysis of Instagram posts tagged with hashtags #covidlife, #covidusa, and #quarantine. 500 most recent selfies from individual accounts between December 1st and 10th in 2021 from age ranges 13 to 55 years old were downloaded for the study. To compare imagery and text content in social media as a function of emotional expression and methods, the study used
open-source two recognition software. IBM Watson tone analyzer uses linguistic analysis to detect emotional tones in written text, and SkyBiometry as emotion detection found in selfie picture. All emotions were counted and categorized into three groups: positive, negative, and pre-affective. In addition,
the study examined whether the emotions explicitly detected was clearly inconsistent when they were visual or textual for the same post. All variables were coded dichotomously (0 = absent, 1 = present) by one coder. For the reliability analysis, we randomly selected 10% of the material. The analysis generally indicated reliable measurement. Krippendorff’s α values were
acceptable all (all values, α > .76).
Variables
Emotions Detection.
The following emotions were coded when they were
detected on the selfie picture: neutral, angry, disgusted, scared, happy, sad
and surprised. If more than one emotion is present, the stronger one is shown.
All emotions were counted and categorized into three groups: positive, negative,
and pre-affective (can be positive and/or negative, depending on the goal
conduciveness of the event).
Linguistic Tone Analysis. The following tones were coded when they were
found in the text post: joy, fear, sadness, anger, analytical, confident and
tentative. If more than one tone is present, the stronger one is shown. All
tones were counted and categorized into three groups: positive, negative, and
neutral.
Inconsistency. The research examined whether the emotions explicitly detected
was clearly inconsistent when they were visual or textual for the same
post. Inconsistency was coded as present (0 = absent, 1 = present). For instance,
when the post presented a positive emotion found in selfie picture with
a contradictory (negative or neutral tone) text post.
Number of Likes. The research explored the relationships between
response behavior and emotions detected from the post. The study collected
and analyzed how many likes each post received.
Demographics. A total of 500 most recent selfies from individual accounts
between December 1st and 10th in 2021 were downloaded from both sex
groups, 250 males and 250 females. The age of the individual was coded,
whether the post included young individuals, older adults, or held no agerelated
information.
RESULTS
The study found that positive emotions are still more prevalent than negative
and pre-affective emotions on Instagram, for both selfie pictures and text
posts during the COVID-19 pandemic. In Figure 6, positive emotions (44%)
were detected on the selfie pictures as 10% more than negative emotions and
22% more than pre-affective emotions. Regardless of biological sex, positive
emotions (54%) were detected on the text posts as 46% more than negative
emotions and 16% more than pre-affective emotions. Compared with
words, the results showed that people are more likely to reveal their negative
emotions (e.g. sadness, angry) on image than text.
Figure 1: Percentage of posts showing emotion categories. N D 500.
The research also examined gender-based differences in emotion expression
on both pictures and words. Among posts, the highest percentage was
positive emotions from females for both visual (28%) and textual (31%)
contents. Within the negative and pre-affective emotions, there were same percentages for males and females found in the text posts. But in selfie pictures,
the percentages of negative and pre-affective emotions from males were
higher in comparison with females.
In terms of the relative numbers of likes received, females were received
2.8 times more likes than males. However, it is important to consider that
there were variety numbers of likes for each post based on the popularity of
an individual. Moreover, the study found that both male and female posted
a significantly larger number of posts (53%) which visually and textual are
inconsistent in emotion expression.
Figure 2: This pie chart illustrates the percentages of each emotion categories detected
on the selfie pictures between males and females.
Figure 3: This pie chart illustrates the percentages of each emotion categories detected
on the text posts between males and females.
Table 1. Instagram post of the total number of likes & inconsistency
between picture and text.
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