Detecting Trends Through Twitter Stream v2

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Trending Topics on the other hand is a list that Twitter provides on it’s homepage. A trend is a type of search; a term that is trending is simply a term that appears in higher frequency than other terms over a set amount of time.

[object Object],[object Object],[object Object]

To apply the appropriate streaming algorithm that will determine the most frequent terms in the Tweet stream

To formulate novelty measurement of trends that will put in consideration the f requency/time element of the terms

To create an application that will implement and help analyze trending topics.

[object Object],Twitter provides an API that can let anyone download stream of data. The stream contains actual tweets from Twitter users around the world at the time of streaming. It provides public statuses from all users that can be filtered in several ways — by userid, by keyword, by geographic location. A stream that is produced by random sampling is needed in this study and the API provides that.

[object Object],In order to determine terms that trend, we need to have a robust scoring method. As discussed earlier, Twitter trends are determined by the degree of its novelty. And from this we can conclude that

[object Object],We propose to use the standard score (also called Zscore) as our method for scoring. The standard score is where x is a raw score to be standardized, μ is the mean of the population and is the standard deviation of the population. In the case of scoring tweet trends: x is the currently observed frequency of a term, μ is the mean of all the historical frequencies of the term and is the standard deviation of the historical frequencies of the term.

[object Object],The Lossy-Counting Streaming algorithm will be used to deal with the very large volume of stream. The algorithm stores tuples which comprise an item, a lower bound on its count, and a ’delta’ () value which records the difference between the upper bound and the lower bound. When processing the ith item in the stream, if information is currently stored about the item then its lower bound is increased by one; else, a new tuple for the item is created with the lower bound set to one, and set to bi/kc. Periodically, all tuples whose upper bound is less than bi/kc are deleted. These are correct upper and lower bounds on the count of each item, so at the end of the stream, all items whose count exceeds n/k must be stored.

By the fact stated, it can be concluded that the terms enumerated are the most frequent terms on Twitter. As a matter of fact Figure 2 shows the consistency of the frequency of these terms to being high. In addition, these terms have high percentage of occurences as shown in Table 5.

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Detecting Trends Through Twitter Stream v2

2. University of the Philippines Los Banos

5. Trending Topics on the other hand is a list that Twitter provides on it’s homepage. A trend is a type of search; a term that is trending is simply a term that appears in higher frequency than other terms over a set amount of time.

10.

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12. Trending Topics module.

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14. To apply the appropriate streaming algorithm that will determine the most frequent terms in the Tweet stream

15. To formulate novelty measurement of trends that will put in consideration the f requency/time element of the terms

16. To create an application that will implement and help analyze trending topics.

17.

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26. By the fact stated, it can be concluded that the terms enumerated are the most frequent terms on Twitter. As a matter of fact Figure 2 shows the consistency of the frequency of these terms to being high. In addition, these terms have high percentage of occurences as shown in Table 5.

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Detecting Trends Through Twitter Stream v2

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