The Blue Planet effect: ANOVA for Twitter data

Introduction

One of my clients graciously donated me this data set with which I worked at her contract. It was mined from Twitter with posts containing a word “bottle” and then sentiment values were evaluated for each tweet. My goal was to investigate if a “Blue Planet II” documentary influence can be detected on Twitter.

The “Blue Planet II” documentary is series of 8 episodes. It debuted on 29 October 2017 in the United Kingdom, Nordic regions, Europe and in Asia. In the United States, the series premiered on 20 January 2018. Other country dates are published here:

https://en.wikipedia.org/wiki/Blue_Planet_II

I have already used the data in one of my previous blogs and you can look at my work here: ’The “Blue Planet II” effect: Twitter data wrangling & ggplot2 plot

Because blogspot.com has a restriction on a post size I will skip pre-processing and a timeline count plot here. I will hide my code this time, too.

Recap of data processing.

Our data span years 2015-2019.

There are different bottles mentioned in tweets: plastic, metal, refillable, recyclable, hot water bottle, insulated, and even seaweed pouches. Bottled water appears as well. I’ve searched tweet texts with regular expressions for such kind of words to determine my categories. I introduced the categories which are described below.

Some of the tweets were classified as “Env concerned” thanks to words “recycl”, “refil” and such. To compensate for misspelling I used approximate matching.

The “Hot water bottle” category contains simple mentioning of hot water bottles.

The “Water bottle” category contains posts where a water bottle kind was not specified.

The data contain very few (below 150) posts with other bottles, like insulated or vacuum or baby. I dropped them.

As we see there are thousands of tweets in the data, which means that we might get reliable statistics.

Bottle type	Count
Env concerned	21207
Hot water bottle	2165
Water bottle	6184

Since the tweets came with evaluated sentiment values we can check if the values differ as result of society views on plastic. The sentiment values range from -0.375 to 0.667 and I calculated averages for each week and year. I plotted the averages. A vertical line represents the documentary debut date. I smoothed out curves for easier trend detection.The colored corridors around them represent how true curves might be off.

My client wanted to see if posters’ sentiments about disposable bottles changed after the documentary. Unfortunately before the fall of 2017 numbers of posts were very few in each category, and ANOVA could not produce a reliable result to detect such change.

Nevertheless we can solve the following question: Do people write with different feelings about different bottle purposes?

For this I will check if the differences of category sentiment value averages are statistically significant. At the moment our plot shows that “water bottle” category is the most neutral, while others are somehow more positive. We will check what statistical analysis can tell here.

ANOVA for Category Sentiment Values.

ANOVA checks how distinct category means of data differ. It is a generalization of t-test for more complicated cases. At first we will check ANOVA assumptions.

1. Interval data of the dependent variable.
   • Our dependent variable representing sentiment values is continuous.
2. Normality
   • We can graph Normal Q-Q plots for categories to see that they are mostly normal, except for some deviations for “Hot Water Category”. Strictly speaking ANOVA numbers might be a bit off, but not much.
3. homoscedasticity
   • There are 2 tests for checking homoscedasticity, or ascertaining that our groups do not have different variances which we can detect from the data: Bartlett test and Levene test. The first one is used when we have normal distributions for our variable groups, and the second is applied when the distributions do not look normal. I did the second one and got F-value as 212.36 and p-value \(< 2.2\cdot 10^{-16}\). Therefore the variances do not differ too much.
4. no multicollinearity.
   • The vectors in question have different lengths, so they cannot be multicollinear. There is sometimes more strict requirement of independence with a remark that this is much harder to check.

Computing ANOVA statistics yielded the F-value as 993 and the p-value as \(< 2.2\cdot 10^{-16}\). Judging by results we can say with a 99% confidence level that the category sentiment value means are not all the same.

In addition we can look up confidence intervals for the differences. I used Tukey method for Honest Significant Differences with confidence level 99%. It computes confidence intervals for mean differences. In addition to calculations the R test function it provides a graph for the intervals to detect if any of them contains 0. As we see all means are different, because differences between them do not include 0.

Shiny app for San Diego coast bacterium counts with Leaflet

 I created another Shiny app. I took for it the same data from San Diego coast stations with counts of unhealthy bacteria which I already used in one of my previous posts. This time a map is interactive.

The map can be moved with a mouse, as any Google map. Using +/- sign it can be zoomed in or out.
You can see marked measuring stations. One can hover a mouse over each and see the station name and averaged amount of measured bacterium count. A bigger size of the mark corresponds to a greater bacterium level. The stations are colored by their groups. Unfortunately I do not know what the group abbreviations mean. On the left one can filter data for a particular kind of contamination, measuring method and choose a whole year or dry or wet season. Not all stations have all measurements, and it shows. The website for the app is below.

Shiny app for San Diego coast bacterium counts

Advantages of Shiny apps are the following:

1) The whole might of R can be used, which means more diverse tools.

2) There are more options (comparatively with Tableau and Power BI) over a Shiny website appearance.

3) It allows quick and easy result sharing for R developers.

Improving my Shiny app for Law of Large Numbers

 I have updated my Shiny app for Law of Large Numbers. Here are the list of changes:

1. Adding Pareto distribution with a mean but without standard deviation. 
2. A choice for a histogram displayed interval, centered around 1.
3. Displayed sample ranges in a plot title.
4. An option to re-generate random numbers used for mean calculations.
5. Made it more pleasing visually by switching background color to more "paper" like and more bookish fonts.

Here is a link to it: https://mathemilda.shinyapps.io/large-number-theorem-visuals/

I will be grateful for your feedback!

Russian Troll Tweet data, Machine Learning with accuracy 99.6%

You can read motivation for my work here: https://myabakhova.blogspot.com/2022/01/russian-troll-data-investigation.html I published EDA, Feature Engineering and Machine Learning on my Kaggle account in 3 parts due to constrains of Kaggle resources:

Part 1. EDA

Part 2. Feature Engineering

Part 3. Machine Learning, a test accuracy 99.6% 

 Conclusion

Analysis of a Twitter account behavior helps a lot in determining paid trolls. The most helpful for detection properties are the ones related to propaganda methods. Apparently trolls have specified guidelines for their posts and they stick to them. I see it as convenient because we can set up filters for catching the most significant phenomena, and then check a whole account activity. 

In addition the most important for prediction features turned out to be not very dependable on languages but mostly on troll account activity. Thus we can do it for other languages, and do not limit it to Russian trolls posting English texts. 

Please upvote it on Kaggle if you like it!