The value(s) of social media rituals: a cross-cultural analysis of New Year’s resolutions

Figures & data

Table 1. Value coding scheme with definitions and examples.

Value	Definition	Example
Money	The valuation of a luxury lifestyle, or the ability to have and spend a lot of money.	New Year’s Resolution: making a lot of money for real and be successful.
Culture	The valuation of making, consuming, and experiencing culture (e.g., books, sports, television, or video games).	It’s a day late but happy new year! This year’s resolution is to just draw …
Self	The valuation of the self, typically in the form of caring for, loving, or expressing oneself.	This year I just believe in myself: That I am lovable, funny, eloquent and also valuable in my melancholy phases. #selflove
Pleasure	The valuation of enjoyment or sensory pleasure.	My 2020 resolution is to chill as much as possible.
Transcendence	The valuation of spiritual practices or religious organizations.	My New Year's Resolution is to keep praying and wait for good things to come.
Relationships	The valuation of close personal relationships, typically in the form of initiating or strengthening connections.	Resolution for 2020: be more present for the people I care about by taking time away from work.
Recognition	The valuation of fame, attention, or other forms of recognition.	Alright here is my new year resolution/goal for 2020! Hopefully getting 50k subscribers on YouTube.
Collective Well-being	The valuation of shared or collective benefit, typically in the context of social or political causes.	My resolution for the year 2020: I want to go to Tohoku. Our debut year was the year of the earthquake so I want to contribute to those areas too.
Effort	The valuation of focus, determination, and effort in pursuit of goals.	Happy new year 2020. My resolution: stay as motivated as possible.
Body	The valuation of health and/or appearance.	This year’s resolution for the first time after living 20 odd years is to ‘become thin.’

Figure 1. Semantic networks of keywords.

Note: Semantic networks of the 50 most frequent words used in 2020 New Year’s resolution tweets in five languages. The size of each node is proportional to its betweenness centrality (a measure of the extent to which it is central to paths in the overall network). The width of a tie between two words is proportional to the number of tweets containing both.

Figure 2. Modes of participation.

Note: Modes of participation in the 2020 New Year’s resolution social media ritual based on a sample of 200 tweets per language (n = 1000). The crosstabulation analysis shows that there is a significant difference between modes across five languages (χ²[12] = 73.23, p < .001). The results of the post hoc analysis are as follows: for resolution, Japanese is a major contributor to the chi-square value (standard residuals [SR] = 5.71, p < .001). For meta-commentary, Italian is a major contributor (SR = 4.20, p < .001) and the German score is a negative predictor (SR = −3.65, p < .001). Lastly, for conversation, German is a positive predictor to the chi-square value (SR = 3.96, p < .001) and Japanese is a negative contributor (SR= −1.71, p < .001).

Table 2. A cross-tabulation of values across five languages.

Values	German	Italian	Japanese	Korean	English	Total	Visualization
Effort	15.0 (−2.10)	14.4 (−2.80)**	25.5 (1.43)	34.7 (5.11)***	16.5 (−1.71)	21.3
Self	21.4 (0.17)	35.6 (6.03)***	17.0 (−1.33)	12.0 (−3.40)***	15.0 (−2.11)	20.9
Culture	12.8 (−2.13)	14.9 (−1.69)	19.4 (0.20)	22.1 (1.29)	25.1 (2.34)	18.9
Body	13.7 (−.09)	10.9 (−1.49)	10.9 (−1.25)	18.2 (2.02)	15.7 (0.81)	13.9
Relationships	6.8 (−1.72)	14.7 (2.41)	13.0 (1.21)	6.8 (−1.98)	10.1 (−0.18)	10.5
Collective Well-being	17.5 (9.54)***	0.6 (−3.41)***	1.2 (−2.39)	0.0 (−3.69)***	6.0 (1.23)	4.4
Pleasure	3.8 (0.98)	4.0 (1.39)	2.8 (0.05)	0.6 (−2.24)	2.6 (−0.15)	2.8
Money	0.4 (−2.08)	1.1 (−1.71)	4.0 (1.37)	2.6 (−0.04)	5.2 (2.63)	2.6
Recognition	3.8 (1.90)	1.1 (−1.19)	2.4 (0.40)	1.3 (−0.94)	2.2 (0.21)	2.1
Transcendence	0.9 (.58)	0.0 (−1.41)	0.4 (−.34)	0.3 (−0.57)	1.5 (2.01)	0.6
Other	3.8 (1.79)	2.6 (.57)	3.2 (1.18)	1.3 (−1.01)	0.0 (−2.39)	2.1

Note. χ² (40) = 306.76, p < .001. *** = p < .001, ** = p < .05. The left number in each cell is the percentage of resolutions adhering to the value from the total number of coded resolutions in a sample of 200 tweets per language (n = 1,000). Numbers written in parentheses are standardized residual scores based on the chi-square posthoc analysis. In the chart (right side), positive predictors of the chi-square value are expressed as blue colors, whereas red colors signify negative predictors of the chi-square value.

Table

Shared keywords	English	Italian	German	Korean	Japanese
Happy	14,210 (10.4%)	177 (2.2%)	65 (1.4%)	141 (1.6%)	56 (2.1%)
Start	8,627 (6.3%)	243 (3.0%)	89 (1.9%)	36 (0.4%)	209 (8.0%)
Health	3,703 (2.7%)	77 (1.0%)	108 (2.3%)	155 (1.7%)	115 (4.4%)
Work/Job	5,242 (3.8%)	426 (5.3%)	79 (1.7%)	342 (3.8%)	133 (5.1%)
Myself	4,461 (3.3%)	142 (1.8%)	45 (0.9%)	–	347 (13.3%)
Friend	3,061 (2.2%)	211 (2.7%)	95 (2.0%)	–	27 (1.0%)
Book	2,197 (1.6%)	109 (1.4%)	28 (0.6%)	–	46 (1.8%)
Love	4,339 (3.2%)	150 (1.9%)	112 (2.4%)	69 (0.8%)	–
Buy	1,381 (1.0%)	43 (0.5%)	47 (1.0%)	43 (0.5%)	–
Twitter	3,962 (2.9%)	173 (2.2%)	201 (4.2%)	241 (2.7%)	–
Continue	1,364 (1.0%)	107 (1.3%)	–	102 (1.1%)	155 (5.9%)
Eat	3,020 (2.2%)	108 (1.4%)	–	92 (1.0%)	59 (2.3%)
Money	1,744 (1.3%)	67 (0.8%)	30 (0.6%)	133 (1.5%)	–
Read	3,025 (2.2%)	207 (2.6%)	80 (1.7%)	92 (1.0%)	–
Smoking	788 (0.6%)	43 (0.5%)	35 (0.7%)	31 (0.3%)	–
Learn	3,377 (2.5%)	–	63 (1.3%)	229 (2.5%)	72 (2.8%)
Spend	1,397 (1.0%)	55 (0.7%)	–	22 (0.2%)	78 (3.0%)
Examples of unique resolutions in each language	God, Mental, Positive, Single, Fitness	Balance Sheets, Graduation, Anxiety, TV, Instagram	Nazis, Politics, Climate Protection, Right-wing party, Environment	BTS, Certificate, Lottery, Physical Energy, Chinese	Feel, Dream, Shrine, Collaboratively, Oshi,

Note. Percentages were calculated based on the total number of tweets by language. This table includes keywords shared in four or more languages - the full list of keywords is presented in the appendix.