How Much is Too Much? Finding the Social Media Sweet Spot

How Much is Too Much? Finding the Social Media Sweet Spot

Introduction

One element of social media strategy, especially for corporate brands, is how often to post messages to your followers. Tweet too little, and you run the risk of disappearing in a user’s feed. Post too much, and you risk annoying your followers and of being unfollowed. Each company’s social team needs to find a happy medium for their market.
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Twitter Reports User Growth, But are the Numbers Correct?

Twitter Reports User Growth, But are the Numbers Correct?

Introduction

Since Twitter went public in November, 2013, they have reported key performance metrics each quarter. For social media – Twitter, Facebook, and LinkedIn – a key performance metric is monthly active users (MAU). MAU, different from the total user base, is how many unique people have logged in that month. This gives an idea of how many people are actually using the network, instead of how many people have simply registered on the network.
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What Went Wrong with the Poll Forecast in Ontario’s Last Election?

What Went Wrong with the Poll Forecast in Ontario’s Last Election?

Introduction

In my two previous posts about the June 2014 Ontario provincial election[1][2], I reported on the results of forecasting opinion polls in real time using Twitter, and compared how that forecast fared against traditional forecasting techniques. Those techniques were very successful, but somewhat unsatisfying for me, personally: I felt they needed to have outside information, such as the aggregate polling history. But they did mirror the polls quite closely. This is fine when the polls are accurate, but they are unable to forecast when the polls are off. For example, in this particular election, the polls were calling for a slight victory by the Liberals, but not enough to give the Liberals a majority government. The election, however, produced a much larger gap between the Liberals and the Progressive Conservatives than predicted.

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How Much Can Social Media Improve Poll Forecasting? You’ll Be Surprised

How Much Can Social Media Improve Poll Forecasting? You’ll Be Surprised

Introduction

In a previous post[1], I discussed the difference between predicting and forecasting, the latter being what I am most interested in. I showed that by using Twitter as an additional source of data it was possible to forecast polls in the June 12 Ontario election 24-48 hours before they are available. The forecasts derived from the Twitter-based model accurately tracked the aggregate polls.

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Right Here, Right Now: How Social Media is Great at Forecasting Polls

Right Here, Right Now: How Social Media is Great at Forecasting Polls

Introduction

Back in June, Ontario held an election for its provincial government and by extension its premier (equivalent to a state governor). There were strong candidates from the three main parties: Kathleen Wynne, the Liberal incumbent; Tim Hudak, representing the Progressive Conservatives; and Andrea Horwath of the New Democatic Party (NDP). Election day was June 12 and the results were 38.7% for the Liberals, 31.2% for the Conservatives, and 23.7% for the NDP.
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