Prediction markets are created for the purpose of making predictions. Predicting outcomes is essential to business decision making. The most famous example meaning is the stock market predictions. People who buy low and sell high are rewarded for improving the market prediction, while those who buy high and sell low are punished for degrading the market prediction. The two most popular approaches for producing reliable forecasts are experts' predictions and "the wisdom of crowds". Nowadays, many companies started using the social media platforms for marketing, custom service and promotions, particularly Twitter.
Twitter scans 10% of tweets at random and then categorizing these according to positive or negative moods, such as ‘alert’, ‘vital’ or ‘happy’. This intelligence is then used to predict movements in the Dow Jones Industrial Average. Incredibly, it’s claimed their method is about 88% accurate for reflecting stock market movements, and importantly it is paying off. This strategy use human emotions to influence and drive the strategy, instead of looking at the effect that these human emotions have on a campaign that has been implemented to drive revenue. Twitter may not be the biggest social network, but they are certainly the most public, as the majority of information in there is available to pretty much anyone, unlike Facebook where it’s unusual to come across a profile that is set to public, with personal updates ready to be searched and analysed.
The Twitter-embedded prediction market can induce people to provide the information easily. For example, sharing information by sending direct messages from smart phones is a more convenient mode of communication. With more people participating and providing more information in a more timely manner, the prediction market potentially can outperform other prediction markets. It seems that Twitter based prediction markets produce better predictions than the prediction markets without social networks. In real life, Twitter Prediction market is not 100% reflect the real outcome. Although the information is provided, we need to adjust the reliability of the predicted information ourselves. Furthermore, in the future, Twitter can do better if they can pay more effort to provide a better algorithm to increase the prediction result and show more monetary value of Twitter.
Reference:
[Website] TweetTrader.net
[Website] Twitter Can Predict the Stock Market
[Website] How Twitter Is Being Used To Predict Movements In Stock Markets
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[Website] Twitter Can Predict the Stock Market
[Website] How Twitter Is Being Used To Predict Movements In Stock Markets
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After attended the lecture 1 to 3 of this course, I have learnt a lot.
First, the course introduced several social experience models. I never knew those types of social platform before. Now, I can classify those social media sites (types and characteristics).
Second, in lecture 3, I have a clear identify the difference between the term (data, information and knowledge) by the different level of cognition.
Third, the most I like is that the example that introduced for the new and developing social task.
I found that social media network really had a big influence to public. We need to analyze the data and found our specific information after processing and form our knowledge.
For further expectation, I want to more social tasks and social strategies that make important for social media users in the future.
Twitter-based prediction markets represent one of the best examples of how this kind of "wisdom of the crowds" can be used. It suggests that carefully designed market mechanisms can bring to light trends in dispersed information that improves the accuracy of in predictions. The information system combines the power of prediction markets with the popularity of Twitter. Simulation results show that the network-embedded prediction market can produce better predictions using information exchange in social networks and can outperform other prediction markets that do not use social networks. Besides, as cost decreases and more and more agents acquire information, the prediction market prices fully incorporate all available information, and the forecasting performance of the network-embedded prediction market is better.
回覆刪除Prediction from social media platform is an interesting topic. However, sometimes accuracy of prediction is hard to improve because many thing are actually in chaostic system. If you study physics, a chaostic system can be influence by a very small factor, just like the weather or the sock market. So very short term prediction can be made, long term prediction is impossible whatever you gather the data.
回覆刪除I just remember a case in this year, about an advertising campaign from McDonald's for promoting the healthy food. McDonald's using Twitter to post a tweet about the healthy food, but finally McDonald's recall the campaign and apologized to customers because many people retweet these advertising tweet and comment about unhealthy actually on McDonald's fast food.
回覆刪除So the prediction can help us to understand the opinions and majority views from internet users.