Difference between revisions of "AY1516 T2 Team AP Methodology"
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A consolidated figure to illustrate how many people who see a particular tweet eventually interact with it (out of the total number of people who saw the tweet), in the following ways/forms: | A consolidated figure to illustrate how many people who see a particular tweet eventually interact with it (out of the total number of people who saw the tweet), in the following ways/forms: | ||
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| + | Although the effectiveness of jokes can be tough to evaluate from a linguistics perspective, our initial approach would be cross referencing the hashtags used in the tweet with Google Trends data (Searches & Events) | ||
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| − | <p> | + | <p>Giving a perspective on the important key variables that affects the popularity of a tweet will aid in the formulation of content that have higher penchant of being a popular tweet</p> |
==<div style="background: #232AE8; line-height: 0.3em; font-family:helvetica; border-left: #6C7A89 solid 15px;"><div style="border-left: #FFFFFF solid 5px; padding:15px;font-size:15px;"><font color= "#ffffff"><strong>Google Trends Analysis</strong></font></div></div>== | ==<div style="background: #232AE8; line-height: 0.3em; font-family:helvetica; border-left: #6C7A89 solid 15px;"><div style="border-left: #FFFFFF solid 5px; padding:15px;font-size:15px;"><font color= "#ffffff"><strong>Google Trends Analysis</strong></font></div></div>== | ||
Revision as of 03:52, 17 January 2016
| Project Description | Data | Methodology |
|---|
Contents
Overview
In the table below we outline the algorithms/techniques that we intend to execute for a particular objective.
| Objective | Analytical Method(s) |
|---|---|
| Network analysis via Degree centrality, Betweenness centrality |
|
| Plan what to publish based on characterisation of audience |
|
Multivariable Regression on tweet content vs Google Trends
With reference to trending topics on a particular day of a tweet, multivariate regression will be performed to relate trending topics to the popularity of a tweet (retweet, likes, etc).
The key variables that we intend to explore are elaborated in the table below:
| Variable | Importance |
|---|---|
|
Retweets |
This measure shows how many times a particular tweet is being shared by followers. We think this is interesting because it highlights the willingness of an individual to share the tweet, increasing the probability that the tweet was interesting. |
|
Url clicks |
This measure shows how many times users actually click on the shortened link shared within a tweet. Given the succinct nature of a tweet, users who click on outgoing links are likely to find the tweet more interesting than other tweets, since clicking on the link would mean interrupting the "flowing" nature while reading the Twitter feed. |
|
Likes |
Compared to Url clicks and Retweets, this measure is the mildest, indicating that the user probably found the tweet interesting, but wasn't compelling enough to share. |
|
Engagement Rate |
A consolidated figure to illustrate how many people who see a particular tweet eventually interact with it (out of the total number of people who saw the tweet), in the following ways/forms:
|
|
Tweet Text |
Although the effectiveness of jokes can be tough to evaluate from a linguistics perspective, our initial approach would be cross referencing the hashtags used in the tweet with Google Trends data (Searches & Events) |
Giving a perspective on the important key variables that affects the popularity of a tweet will aid in the formulation of content that have higher penchant of being a popular tweet
Google Trends Analysis
In planning the content for the upcoming quarter, the content management team typically uses Google Trends to understand consumer trends in both past similar quarters as well as the present. They would also also consider the present context of festivities and events.
Content Themes Analysis
Skyscanner has identified 7 content themes articles typically belong to. Operating on a lean workforce, it would be helpful to be able to identify which of the 7 content themes reaps the greatest yield. Here, we define yield by the metrics Google analytics tracks. They are the number of unique page views, bounce rate and exit %, as well as the average time spent on page. This will be done via Text Miner by SAS.
Text Miner can generate a number of topics. Each topic will be associated with a set of representative keywords derived from the corpus of articles input to the algorithm. Each article would have a probability rating of belonging to a particular topic. We would tag the topic with the highest probability rating to the article. We would then manually examine the keywords representative of the topic, then classify the topics according to the 7 content themes. Having classified the articles into the 7 content themes, we can now analyse them with the google analytics metrics, thereby identifying popular content themes as an area of focus.
