Behavior Analysis: #cebu for Cebu community(January 2018)

Introduction

The goal of this analysis is to show the activity of #cebu in order to assess the performance and behavoir in Steemit on January 2018. The analysis is intended to see the performance of the tag #cebu as in terms of a comparison to the last Quarter of 2017. It also includes daily behavior of #cebu
in terms of no. of authors, post, sum of total payout, comments, and upvotes.

Outline

I. Scope of Analysis

II. Result

1. Data presentation and comparison ( 2017's Last Quarter vs January 2018)
   2. Daily Statistics for January 2018
   3. Top 10 used Tags, Author by Payouts and Upvotes
   

III. Summary

IV. Tools and Codes

I. Scope of Analysis

The analysis is performed on data extracted from @arcange 's SteemSQL database. There are three sets of data used:

• A five month data for #cebu that includes the last quarter of 2017 (September to December), and January 2018;
• A n daily statistical data for #cebu in January 2018;
• A data that includes all the post and variables for #cebu.

The analysis of the extracted data involves taking averages for each categories/ variables; and, comparing the January 2018 data to the 2017's last quarter averages. This is also done to assess the performance of #cebu. The data then presented using charts and tables, as applicable. In addition, the analysis also includes 3 categories of TOP 10 instances such as top tags, author based on highest payout and, author based on no. of upvotes. The analysis also present a summary of all result.

II. Result

The following data presented below either tabulated or charted is based on the data extracted from SteemSQL as of February 16, 2018 ( 9 AM UCT).

1. Data presentation and comparison (2017's Last Quarter vs January 2018)

In January 2018, the tag #cebu was used by 602 authors whom generated a total number of post , comments, upvotes, and payouts equal to 3684, 19029, 52412 and 14864.851 respectively. This numbers are summation of all accumulated post, comments, upvotes, and total payout received by 602 distinct authors.

Table 1.1: Monthly statistic for #cebu inclusive for September 2017 up to January 2018

The Table 1.1 show the number of authors per month; and, sum of the no. of post, comments, upvotes, and, payout for the inclusive dates. It has an observable increase in all of the categories from the January 2018 as compared last quarter of 2017 (September-December).

Graph 1.1: Monthly statistic for #cebu inclusive for September 2017 up to January 2018

From the graph 1.1 and table 1.1, we can draw a close to 25 distinct authors per month on average that uses the tag #cebu. Close to 23.38% increase is observe in the number of authors using the the tag #cebu is observe in January 2018, amounting to 602 authors. In terms of total payout, we can account an approximate 27.33% increase from the the last quarter's 537.358 (SBD and SP) on average as compared to 14,864.851(SBD and SP) in January 2018 data. For the comments, a 19029 number of comments generated in January 2018 that closes 41.01% increase from the last quarter of 2017 avergae. We can also account a 54.55% increase from the last quarter's 1177 upvotes on average to a 53412 upvotes generated in January 2018.

2. Daily Statistics of January 2018

In Table 2.1, we can draw an 85 authors per day on average using the tag #cebu. This average authors generates close to 199 daily posting which receive approximately 614 comments, 1691 upvotes and 479.51 payout in SBD and SP.

Table 2.1: Daily statistic for #cebu in January 2018

Graph 2.1: Line graph for No. of post and author

From graph 2.1, we can observe a positive data trends for the number of authors using the tag #cebu On average, there are 85 authors using the tag and generating a average posting close to 199. We can also observe a sudden influx of usage from January 17-22, 2018. We can account 139 post on average from the said time frame. Overall, we can observe a positive trends between the two categories, even with there are high and low in between January 1 - 31, 2018.

Graph 2.2: Frequency distribution(Histogram) of Sum of Total Payouts on January 2018

In January 2018, an average sum of total payout of 479.51 in SBD and SP is received by #cebu daily. We can see a normal distribution curve from the histogram shown in graph 2.2. The data from sum of the total payout is concentrated between 349.493 to 669.493 in SBD and SP. It means that 54.84% or 17 of the daily accumulated post using the tag #cebu accumulates a total payout between 349.493 to 669.493 in SBD and SP.

Graph 2.3: Line Graph for Daily Sum Comments and Upvotes on January 2018

If we take a closer look at the comments and upvotes, we can draw a conclusion that the comment generated per post is in some way dependent to the number of upvotes receive by the post. On average, using the tag #cebu, each accumulated post received an average sum of comments equal to 614 on a daily basis. The post s also generate a daily average sum of upvotes equal to 1691.

Graph 2.4: Daily Frequency Distribution of Upvotes on January 2018

We can see a normal distribution curve from the histogram shown in graph 2.4. The data from sum of the total comments is concentrated between 1275 to 2475 comments. It means that 38.71% or 12 days of the daily accumulated post using the tag #cebu accumulates a total comments between 1275 to 2475 comments.

Graph 2.5: Daily overall statistics on January 2018

Generally, the daily statistics for #cebu shows a very positive trends in terms of the number of accumulated authors, posts, and total payouts with daily averages 85, 199, and 479.51 payout in SBD and SP, respectively. However, for comments and upvotes, a steady or nominal trend is observable having the following averages as 614 and 1691 respectively.

3. Top 10 Statistics in January 2018

In this section of the analysis, we sill into the top 10 tags used along with #cebu, Top 10 authors based on highest payout and net votes received.

3.1 Top 10 Tags used along #cebu.

Alongside #cebu, the tags #poetry, #utopian-io, and #photography recieves 292, 178, and 175 posts respectively. Rounding up the top 10 are #philippines. #cebu (redundancy) , #food, # life, #sevendaybnwchallenge , #travel and #art.

Author	Highest total_payout_value
poetry	292
utopian-io	178
photography	175
philippines	173
cebu	167
food	135
life	127
sevendaybnwchallenge	115
travel	113
arts	96

The top 10 tags comprises 46% of the total number of post, while the other tags that comprises 54% of the total number of post.

3.2 Top 10 Cebuano Author based on Highest Total Payout

Author	Highest total_payout_value
@purepinay	753.847
@jassennessaj	187.899
@steemph.cebu	121.624
@namrana	121.6124
@ybanezkim26	98.292
@pizzanniza	90.836
@shoganaii	86.79
@ligarayk	81.312
@nenyea	73.137
@deejee	72.46

The highest paid author that used the tag #cebu is @purepinay whom accumulated a highest reward 753.847 in SBD and SP, while @jassennessaj and @steemph.cebu (cebu community account) received 187.899 and 121.624 respectively rounding up the top 3. The other authors who joins the rank are @namrana, @ybanezkim26, @pizzanniza, @shoganaii, @ligarayk, @nenyea, and lastly, @dejee.

3.3 Top 10 Most upvote Cebuano Author

Author	Highest Net_votes
@purepinay	1916
@jonnahmatias	1033
@emonemolover	128
@bonjovey	156
@jassennessaj	148
@sorenkierkegaard	140
@marbul21	121
@knthalo	119
@engrdrdarwin	115
@deejee	113

As usual, @purepinay ranks first at 1916 upvotes. It is then followed by @emonemolover, @bonjovey, @jassennessaj, @sorenkierkegaard, @marbul21, @knthalo, @engrdrdarwin and lastly, @deejee. However, landing as the most upvoted cebuano authors does not translate to exceptional rewards. It is evident that only three from the list has received the highest total payouts, namely @purepinay, @jassennessaj, and @deejee.

III. Summary

Cebu community has become more active on Steemit in January 2018 as compared with the last quarter of 2017 . Overall,the January 2018 data points out a positive trends as compared to the previous values from the last quarter of 2017. From the daily average, #cebu shows a very positive performance trends in having an accumulated authors, posts, and total payouts equal to 85, 199, and 479.51 payout in SBD and SP, respectively. Nominal or slightly above norms in number of comments and upvotes received by #cebu with averages equal to 614 and 1691 respectively. In addition, it is also observe that the daily #cebu posting in January 2018 lies within a sum of total payout value of 349.493 to 669.493 in SBD and SP. Therefore, #cebu tag is performing better and bring positive trends in terms averages in no. Of authors, post, comments, upvotes and payouts.

IV. Tools and Codes

I extracted the data from SteemSQL by importing it on Microsoft Excel using SQL Queries. I used Microsoft Excel to visualize and create a graph for the data. The data is extracted by using COUNT() and SUM() function from json_metadata. The variables being treated are authors, no. of post, comments, total payout, and net votes. The full SQL script is shown below.

SQL query script for Overall January 2018 Statistics
SELECT COUNT ( DISTINCT author ) AS Author, COUNT (*) AS Posts, sum(children) AS Comments, sum(net_votes) AS Upvotes FROM Comments (NOLOCK) WHERE parent_author = '' AND created >= CONVERT(datetime,'01/01/2018') AND created< CONVERT(datetime,'02/01/2018') AND depth = 0 AND json_metadata LIKE '%"cebu"%'

SQL query script for Overall Monthly
SELECT MONTH(created) AS MOTH, COUNT ( DISTINCT author ) AS Author, COUNT (*) AS Posts, sum(children) AS Comments, sum(net_votes) AS Upvotes FROM Comments (NOLOCK) WHERE parent_author = '' AND created >= CONVERT(datetime,'09/01/2017') AND created< CONVERT(datetime,'02/01/2018') AND depth = 0 AND json_metadata LIKE '%"cebu"%' GROUP BY month(created)

SQL query script for Overall Daily January 2018 Statistics
SELECT DAY(created) AS DAY, COUNT ( DISTINCT author ) AS Author, COUNT (*) AS Posts, sum(children) AS Comments, sum(net_votes) AS Upvotes FROM Comments (NOLOCK) WHERE parent_author = '' AND created >= CONVERT(datetime,'01/01/2018') AND created< CONVERT(datetime,'02/01/2018') AND depth = 0 AND json_metadata LIKE '%"cebu"%' GROUP BY day(created)

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Posted on Utopian.io - Rewarding Open Source Contributors

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