An Overview Of Tag Cloud Visualizations Computer Science Essay

Tag clouds have become really popular visual image and seeking interface. They have many advantages depend on the user end. On the other manus, ticket clouds have disadvantages. This paper describes the ticket clouds visual image. In add-on, it lists the advantages and disadvantages of ticket clouds visual image and it shows the type of undertakings that are appropriate for these tickets.

Keywords:

Tag Clouds, Social media, Advantages, disadvantages, visual image, Folksonomy.

Introduction

The recent popularity of ticket clouds has become from the thought of societal media labeling besides known as folksonomies. Folksonomy is defined as a categorization system, came from pattern and methods of making and pull offing tickets to explicate and categorise content. Harmonizing to Mathes ( 2004 ) , tag cloud have become really popular because of they use as web base visual image of the keywords or tickets. These tickets, visualise the frequence of most common words used, so they doubles as an index for accessing any content categorized by each ticket. Hassan-Montero and Herrero-Solana ( 2006 ) define ticket clouds as “a list of the most popular tickets, normally displayed in alphabetical order, and visually weighted by font size” . In other words, it ‘s a aggregation of words of different sizes and color all are gathered in a cloud or a group. A ticket that is larger and has darker coloring material is more popular than a ticket that is smaller and has a lighter coloring material. This belongings is based on either the volume of information that is related to the ticket or based on the popularity of this ticket.

Factors that Affect the Usability of Tag Clouds

These yearss, there are many tools that can be used to bring forth ticket clouds. The effectivity and serviceability of these tools to bring forth sufficient tickets depend on different factors:

1. Font Size: The fount size has a great affect on the user who is seeking to look for a ticket. Harmonizing to Rivadeneira et Al. ( 2007 ) , tags that ordered in frequence have the best consequence on the user.

2. Alphabetization: arrangement tickets in alphabetic order aid the user to happen the tags faster. Many users prefer to scan for tickets instead than look intoing the font size or the coloring material. ( Halvey & A ; Keane, 2007 ) .

3. Some Certain ocular characteristics: Such as coloring material, font weight and size influence how fast we can happen a ticket. Tag the bash non hold these characteristics will impact the serviceability of any ticket ( Bateman, Gutwin, & A ; Nacenta, 2008 ) .

Advantages of Tag Clouds Visualizations

Harmonizing to Miller ( 1996 ) , the construct of utilizing keywords that is non assigned by an expert is non new. Many diaries use writer generated keywords to make indexing. Besides the usage of cardinal words was a really old characteristic of World Wide Web “WWW” . In ticket clouds, on the other manus, users are who create the tickets alternatively of the writers. This is because users want server their demand. As a consequence, a consensus vocabulary emerges. Another advantage of ticket clouds is that they visible to other users. If any user has the same involvement of any labeled user, he / she can entree the same resource. This means people can assist each other by utilizing ticket clouds. Another advantage utilizing ticket clouds is they are really simple and straightforward. Tags do non necessitate an expert to utilize it ; users can label the words that they are looking for to happen what they desired. Furthermore, Tags are multidimensional. Taggers can take a big figure of tickets to explicate a construct so unite them. Another of import advantage harmonizing to Hayman & A ; Lothian ( 2007 ) , labeling provide gives really utile information for organisations. These informations can be related to country of involvement, the demand of the communities and people wont. Another concern advantages have been identified by Clay Shirky ( 2005 ) and these are as follow:

* Market Logic:

As we get used to the deficiency of physical restraints, as we internalize the fact that there is no shelf and there is no disc, we ‘re traveling towards market logic, where you cover with single motive, but group value. As Schachter says of del.icio.us, “ Each single classification strategy is worth less than a professional classification strategy. But there are many, many more of them. ” If you find a manner to do it valuable to persons to label their material, you ‘ll bring forth a batch more informations about any given object than if you pay a professional to label it one time and merely one time. And if you can happen any manner to make value from uniting countless recreational categorizations over clip, they will come to be more valuable than professional classification strategies, peculiarly with respects to robustness and cost of creative activity.

· User and Time are Core Distribute.

* Signal Loss from Expression:

The signal loss in traditional classification strategies comes from compacting things into a restricted figure of classs

* The Filtering is Done Post Hoc:

the thought that the classification is done after things are tagged is improbably foreign to catalogers.

* Merged from URLs, Not Classs:

This allows for partial, uncomplete, or probabilistic merges that are better tantrums to uncertain environments than stiff categorization strategies

* Merges are Probabilistic, non binary:

Merges create partial convergence between tickets, instead than specifying tickets as equivalent word.

In societal media position, there many advantages utilizing tickets clouds visual images and these are:

* The categorization system is generated by the community:

as we province before, the users are sorting the ticket.

* The categorization systems develop:

* Community tendencies can be monitored:

what people are labeling can be tracked over clip

* Improved trade name trueness:

as users who feel they are involved with a trade name tend to be loyal to them

* Non linear shoping and improved searching:

alternatively for regular browse and searching, users can bask seeking utilizing the visual image. In add-on, it will non necessitate any accomplishment to make the searching.

Disadvantages of Tag Clouds Visualizations

We have discussed the benefits of both labeling and tag clouds but Lashkar-e-Taiba ‘s take a expression at some of the disadvantages:

First, the simpleness of tagging and the easiness of usage will take to really hapless tickets. Harmonizing to Hayman & A ; Lothian ( 2007 ) , it could be argued that this is an of import characteristic of tickets but the issue demand to be considered for future probe. Second, tickets can be applied in different ways with the same significance. In other words, cats may be used in one instance and pool in the other 1. Third, about all labeling systems do non hold regular indexing or cataloguing regulations. To explicate it more, labeling system does non use regulations such remarkable or plural signifiers. Another disadvantage is many users tend to utilize the popular tickets instead the accurate 1. They may non gain that there are more accurate footings to utilize alternatively of these tickets. In 2005, Zeldman states that” Network effects being exponential, what is instantly mildly popular rapidly becomes unnaturally really popular, while what has yet to go popular ne’er will be” . Harmonizing to Sinclair and Cardew-Hall in their article “The Folksonomy Tag Cloud: When is it utile? ” published in 2007, tag clouds were less utile when seeking for specific information. They besides noted that replying inquiries utilizing ticket clouds require more questions per interface. They said that is preferred to utilize a hunt box for replying inquiries.

Another disadvantage is tag cloud occlusion. Tag clouds do non let any direct entree to all articles in any database. When any tags question consequence is shown, it shows the co-occurring tickets as screening in Fig.2. By snaping in any of the tickets, it will show an article. Any article that had no tickets in the forepart ticket cloud it will non be displayed. They besides prove that ticket clouds are non sufficient because their inability to do all articles accessible. They besides prove that the per centum of occlusion remained approximately changeless at approximately 55.5 % for each session. Fig.3.

Type of Tasks that are Appropriate for Tag Clouds

Decision

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