Search (2 results, page 1 of 1)

0.01220762 = product of:
  0.04883048 = sum of:
    0.04883048 = weight(_text_:social in 2563) [ClassicSimilarity], result of:
      0.04883048 = score(doc=2563,freq=2.0), product of:
        0.1847249 = queryWeight, product of:
          3.9875789 = idf(docFreq=2228, maxDocs=44218)
          0.046325076 = queryNorm
        0.26434162 = fieldWeight in 2563, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.9875789 = idf(docFreq=2228, maxDocs=44218)
          0.046875 = fieldNorm(doc=2563)
  0.25 = coord(1/4)

Abstract

Topic discovery is an important means for marketing, e-Business and social science studies. As well, it can be applied to various purposes, such as identifying a group with certain properties and observing the emergence and diminishment of a certain cyber community. Previous topic discovery work (J.M. Kleinberg, Proceedings of the 9th Annual ACM-SIAM Symposium on Discrete Algorithms, San Francisco, California, p. 668) requires manual judgment of usefulness of outcomes and is thus incapable of handling the explosive growth of the Internet. In this paper, we propose the Automatic Topic Discovery (ATD) method, which combines a method of base set construction, a clustering algorithm and an iterative principal eigenvector computation method to discover the topics relevant to a given query without using manual examination. Given a query, ATD returns with topics associated with the query and top representative pages for each topic. Our experiments show that the ATD method performs better than the traditional eigenvector method in terms of computation time and topic discovery quality.

0.010173016 = product of:
  0.040692065 = sum of:
    0.040692065 = weight(_text_:social in 967) [ClassicSimilarity], result of:
      0.040692065 = score(doc=967,freq=2.0), product of:
        0.1847249 = queryWeight, product of:
          3.9875789 = idf(docFreq=2228, maxDocs=44218)
          0.046325076 = queryNorm
        0.22028469 = fieldWeight in 967, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.9875789 = idf(docFreq=2228, maxDocs=44218)
          0.0390625 = fieldNorm(doc=967)
  0.25 = coord(1/4)

Abstract

Because of Twitter's popularity and the viral nature of information dissemination on Twitter, predicting which Twitter topics will become popular in the near future becomes a task of considerable economic importance. Many Twitter topics are annotated by hashtags. In this article, we propose methods to predict the popularity of new hashtags on Twitter by formulating the problem as a classification task. We use five standard classification models (i.e., Naïve bayes, k-nearest neighbors, decision trees, support vector machines, and logistic regression) for prediction. The main challenge is the identification of effective features for describing new hashtags. We extract 7 content features from a hashtag string and the collection of tweets containing the hashtag and 11 contextual features from the social graph formed by users who have adopted the hashtag. We conducted experiments on a Twitter data set consisting of 31 million tweets from 2 million Singapore-based users. The experimental results show that the standard classifiers using the extracted features significantly outperform the baseline methods that do not use these features. Among the five classifiers, the logistic regression model performs the best in terms of the Micro-F1 measure. We also observe that contextual features are more effective than content features.