Difference between revisions of "Huang 2010 Conversational Tagging in Twitter"

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* ''Conversational vs. Organizational''
 
* ''Conversational vs. Organizational''
dadda
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Tagging practices in Twitter are an example of a new type of
 +
tagging, which the authors call them ‘conversational’ tagging. In
 +
conversational tagging, the tag itself is an important piece of the
 +
message. The tag can either serve as a label in the traditional sense
 +
of a tag, or it can serve as a prompt for user comment. In many
 +
trending topics, Twitter tags sometimes serve as prompts, and the
 +
resulting content is an asynchronous massively-multi-person
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conversation. While these are not the only types of tags used in
 +
Twitter, the authors argue that this is a type of tagging behavior that
 +
emerged due to the structure of the Twitter system.
  
 
* ''Micro-memes''
 
* ''Micro-memes''

Revision as of 22:10, 31 March 2011

Citation

Jeff Huang, Katherine M. Thornton and Efthimis N. Efthimiadis. 2010. Conversational Tagging in Twitter. In Proceedings of ACM HT.

Online version

An online version of this paper is available at [1].

Summary

This paper presents a study of Twitter tags versus tags in other Web 2.0 systems. They show several findings on their differences and similarities. They claim that twitter tags are more about filtering and directing content so that it appears in certain streams.

Key Contributions

The paper made a key contributions by its findings on the differences between twitter tags and tags in previous systems. It presents the old-style tags as a posteriori and the twitter-style tags as a priori. As claimed by the authors, it is the first large-scale study on twitter tags.

Dataset

They author created their own dataset, from 2 different sources: Twitter and Delicious. They collected a sample of 42 million hashtags used in the microblogging website Twitter, inserted in messages posted by users. They also got a sample of 378 million tags from the online bookmarking service Delicious, created by users to organize their bookmarks. Both of these datasets contain the tag along with the timestamp of when that tag was attached, intended for temporal analysis.

Qualitative Analysis

The authors first present their qualitative analysis of the tags used in Twitter and Delicious. The authors went through the 224 most common tags in the Twitter dataset and the 304 most common tags in the Delicious dataset. And they show three key insights:

  • Trending Effect

Twitter started displaying trending topic information on their front page. These trending topic lists are individually linked to the current set of tweets composed on that topic. While tweets without hashtags were also displayed in trending topic lists, the act of tagging a tweet increased the likelihood of a tweet being displayed in a group of tweets on a trending topic.

  • Conversational vs. Organizational

Tagging practices in Twitter are an example of a new type of tagging, which the authors call them ‘conversational’ tagging. In conversational tagging, the tag itself is an important piece of the message. The tag can either serve as a label in the traditional sense of a tag, or it can serve as a prompt for user comment. In many trending topics, Twitter tags sometimes serve as prompts, and the resulting content is an asynchronous massively-multi-person conversation. While these are not the only types of tags used in Twitter, the authors argue that this is a type of tagging behavior that emerged due to the structure of the Twitter system.

  • Micro-memes

dadd

Quantitative (Statistical) Analysis

The authors present details on their sentiment classification experiments including the feature extractions, classifier building and experiments results.

In feature extraction, authors present a four-step approach consisting of (1) filtering URL links, Twitter user names and such non-informative tokens; (2) tokenizing the text with punctuation marks and spaces; (3) removing stopwords (articles); (4) constructing n-grams.

In classifier building, the authors claim that they have tried Naive Bayes, SVM and CRF. However, Naive Bayes classifier works the best thus was picked.

In the final results, the authors present several comparisons between systems with different settings and conclude that the Naive Bayes classifier with bigram features works best due to its good balance between coverage and sentiment patterns.

Discussion

This paper gives a broad overview of twitter hashtags, in particular from user's perspective. It is thus highly related to our proposed course project on automatic Twitter message clustering based on hashtags.