Steemit Business Intelligence: Tags Specific to Indonesia Trends & Distribution + Comparative Analysis

Since joining Steemit midway through July, I have been noticing the use of the tags #aceh and #indonesia. I did not know how to use SQL at that time, but I've been deeply interested to understand how Indonesia, a Southeast Asian country like the Philippines has been able to create a considerable dominance early into Steemit's infancy.

In this analysis I will try to present the trend and distribution of both count of post and total_payout_value between the tag #indonesia and #aceh.

Having done a similar analysis on Philippines and Malaysia related tags, this contribution also presents an opportunity to do a comparative analysis between the three Southeast Asian countries in terms of activity, user-base, and total_payout value using the tags citizens of those nations use as an identifier of their location or country of origin.

Aceh is an Indonesian province located in the northern end of Sumatra. Although this data may not fully support it, it looks like adaption of Steemit started in that region until the participation from the wider Indonesia picked up in June of 2017.

Delimitation

This analysis will present the distribution and trend related to post count and total_payout_value of #aceh, and #indonesia categories. The data will only be captured then if any of those were used as the primay tag. The analysis will also not filter the data by author to determine if he/she is from the Indonesian Province of Aceh or based in Indonesia.

The analysis will only capture the data for the posts where the tags were used and will filter out comments in calculating the count of posts, and total_payout_value.

In the comparative analysis, this contribution will capture population of each of the countries being compared, the recorded internet user, the posting activities based on the tags identified, the total_payout_value summed up per country specific tags, and the user-base using the country specific tags.

I used arcange's Steem SQL Public Database to acquire the data-points related to usage of tags from the Comment table for the Steemit related data, http://www.worldometers.info/world-population/ for the population related data, and https://www.statista.com for internet usage related data.

Methodology

Like in the similar contributions analyzing Philippines and Malaysia specific tags, I extracted the data related to each of the tags that are subjects of this analysis by running these simple SQL queries:

SELECT *
FROM Comments (NOLOCK)
WHERE category in ('aceh')

SELECT *
FROM Comments (NOLOCK)
WHERE category in ('indonesia')

To get the month and year date format from default date time format, I used =TEXT(timestamp,"mmm-yyyy") in excel

I removed the comments by running the =RIGHT(Text,Number of Characters) formula in excel and filtered out the result beginning with re-.

I then plotted the data-points in an excel spreadsheet to create the charts and visually present the data.

The data is complete until the end of December 2017, and there was not need to extrapolate anything the figures used in this analysis are all actual results.

The Analysis

In the first chart, we have seen the #indonesia tag taking over the #aceh tag in terms of posting activity in June of 2017. In the above chart though, the take over actually took a month earlier in May of 2017.

In the next two charts we will see a clear shift in the distribution of total_payout_value between April to December 2017. The #aceh tag had 65% share of the total_payout_value in April, by December it only got 4% share of the total_payout_value.

In the chart below, I listed the Top 20 Authors (in terms of total_payout_value) who used #indonesia tag. Here are key observations with some contrast against the observations from #teammalaysia Top 20.

Out of the total_payout_value of $5,030.04 between July 2016 to December 2017, $3,992.22 (79.37%) were generated from the top 20 out of 191 authors who used #teammalaysia as a primary tag.

• Only $17,162.10 (31.38%) out of the total_payout_value of $54,697.65 went to the top 20 out of 2869 authors who used #indonesia as a primary tag. This may look smaller, but this is only because the top 20 in #teammalaysia is 10.47% of the 191 authors, while top 20 in #indonesia is only 0.70% of the 2869 authors. When the whole total_payout_value is divided by the number of authors, it will be $26.34 for #teammalaysia and $19.07 for #indonesia. At the average the top 20 in #teammalaysia makes 758% ($199.61) of $26.34, while the top 20 in #indonesia makes 4501% ($858.11) of $19.07.

In #teammalaysia top 20 there were 3 Sndbox fellows and 1 Sndcastle.

• In #indonesia top 20 there are 4 Sndbox fellows (aiqabrago, levycore, ayijufridar, and kemal13)

To do a comparative analysis of the three Southeast Asian countries in term of performance, I used only the widely used tags per country; #philippines for Philippines, #teammalaysia for Malaysia, and #indonesia for Indonesia.

I looked at the potential to grow user-base first using population and internet users count statistics. Based on the two charts below, #indonesia have the greatest potential between the 3 countries in terms of both population and count of internet users.

While #indonesia clearly got an advantage in terms of growing user-base having 62% share of internet users, they are clearly also growing much quicker than #philippines and #teammalaysia having 78% share of userbase using the #indonesia tag.

The share of total_payout_value is directly proportional to the user-base using the country tag for the Philippines, both are of 7% share. As called out earlier, at the average the total_payout_value for authors when using #teammalaysia tag is $26.34 compared to $19.07 when using #indonesia. This is causing a slightly higher share of the total_payout_value when compared to the number of users using the country tag for #teammalaysia.

Conclusion

The data-points presented here shows the growth of the Indonesian Steemit community, and how adaption seem to have started in the specific region. Furthermore, it supports an earlier conclusion about how Sndbox is driving growth in communities.

The cross-analysis between the country being analyzed in this contribution, and the two other countries previously analyzed shows that growth potential is not merely based on population and count of internet users. While Indonesia has the higher share of both population and count of internet users, managed to get an even higher share of user-base and total_payout_value. This should be taken as an inspiration for the two other countries to strengthen their promotion efforts.

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Posted on Utopian.io - Rewarding Open Source Contributors

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