View source for Huang 2010 Conversational Tagging in Twitter

== 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 [http://portal.acm.org/citation.cfm?id=1810647].

== Summary ==

This [[Category::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''
An interesting case called "micro-meme" was presented by the authors. They picked #igrewupon, #liesmentell, #igottacrushon and #90stweet as example that were observed in Twitter associated with
emergent micro-memes. These hashtags are rarely used to retrieve
old tweets; instead, they provide synchronic metadata used to
funnel related tweets into common streams.

== Quantitative (Statistical) Analysis ==
The authors conducted the statistical analysis, mostly on the temporal effects of those twitter hashtags and presented the results on standard deviation, skew and kurtosis of the hashtagged messages' timestamps.

From the standard deviation, they find that a low standard deviation is a good
indicator that the tag is used for conversational (i.e. social) rather
than organizational purposes. On the other hand, the tag with high standard deviation tends to represent a group of topically related tweets. They show two figures using the skew to illustrate the gradual adoption of the tag #twitterafterdark and a slow abandonment of #postcrossing over the year of 2009. From the fourth moment, kurtosis, they use two figure to illustrate how to measure tags' staying power.

== 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.