[MMDays Special Column] Web [email protected] : WARM – The Interpersonal Relations on the Web

Posted by Mr. Wednesday

wam logo

Website: WARM – The Interpersonal Relations on the Web

Services Provided:

Blogs are so popular in Taiwan these years. Someone who doesn’t blog is just like that he does not have name. Blog has become the common way to know people and to be known by others. Many blog service providers (BSP) provides the friend list feature; in other words, users can set other user as his/her “friend”. Obviously, many direct or indirect relation links form complicated social network on the Web. We all have desire to know the map of relationship. Therefore, social network search engines are coming to fulfill this demands.

WARM is directed by Dr. Wu (CSIE of Shu-Te University) and the core team includes CSIE students, 許無寒, 許迺赫, 李紀廣 and 管世達. The whole project was completed in 2007. Originally it is just a research project in campus; then it suddenly becomes so popular in Taiwan.

Functions Review:

WARM can search the following social networks: Wretch, Yam Blog, Xuite and Pixnet. (Wretch is the biggest one in Taiwan and it dominate the 70% market share.) According to the homepage of WARM, it provides the following functions: (1) Who set me as friend, (2) relation searching, (3) celebrity ranking, (4) co-cited Friend, (5) similar friends and (6) friend group. The following tests are focused on Wretch and the account, “ksbcboy”, is used to do the experiment.

  1. Who Set Me As Friend
    The so-called friendship is just a directed edge in graph; in other words, people can decide whom to be their friend but they cannot decide who will set them as friend. This function is for finding back links of friendships. In other words, find out who set me as his/her friend.

    誰加入我

  2. Relation Searching
    Stanley Milgram is a social psychologist at Harvard University. In 1976, he began a widely-publicized set of experiments to investigate the so-called “small world problem”. The six degrees of separation concept originates from this experiment. It means any two persons on earth can be connected through a chain of acquaintances that has 6 intermediaries in average.

    six degrees of separation
    (from: Wikipedia)

    This function of WARM is to find the distance between two persons. To find out through how many persons can connect two unfamiliar persons.

    關係搜尋

  3. Celebrity Ranking
    Finding out the so-called celebrity (You know him/her but he/she doesn’t know you) according to how many times he/she is set as friend by others.

    萬人迷排行榜

    Mr. Wednesday noticed that user can give comments to the other user. It can help others to know this person; however, there are some emotional comments (not good words…) and Mr. Wednesday thinks that this function is not practical.

    評論

  4. Co-cited Friends
    Based on people who set someone as friend, find out whom these people also set as friend. In other words, find out who is usually set as friend with you. Take the following result for example, there are 4 people (25%) who set “ksbcboy” as friend also set “ruo616” as friend.

    相似人氣

  5. Similar Friends
    Find out someone who has similar friends. In other words, find out whom has similar social contact with you. Take the following result for example, among “ksbcboy’s” friends, there are 10 persons same with “utechkimo’s” that occupies 32% of “uetechkimo’s” all friends.

    相似好友

  6. Friend Groups
    Find out some groups in which people know each other.

    好友群

UI/Usability Review:

WARM regards itself as a social network search engine currently. All functions begin from a query and end in a search result. In terms of searching, the presentation of search results is compact and it is easy to use. However, the search results are lack of further explanation and applications. The practical utility is still not enough.

During the testing, the response time of server is short but sometimes the searching is freezing till timeout. This is really harmful for services on the Web.

Similar Services:

There are some similar social network search engine. They search the whole Web and analyze the search results to organize the information of persons, such as PeekYou, Pipl, Spock, ucloo, WikiYou, Wink and Zoominfo, etc. Among them, Spock got 7 million USD form VC last year even it was not open at that time.

Besides, there are more and more social networks on Web, such as Facebook, Friendster, LinkedIn, MySpace, orkut, Yahoo! 360, wallop and Xiaonei, etc. The information on these websites are provided by users while registering or using. There is a standalone social network in each website so the searching is limited in individual websites. The standpoint of WARM is different from them.

Profitability:

WARM is free for use currently.

Conclusion:

The source of information for WARM comes from the blog service providers and it’s not easy to collect the data. There is no business model and profitability. It’s still focused on interests and academia research. In conclusion, WARM is a surprising service but it’s not good enough to earn profits.

By this kind of social network search engine, the interpersonal relations between people seem become “clear”; However, the innominate nature of Web is broken. Everything you want others to know or not could be discovered. A few months ago, there is a news in Taiwan, it reads: A Girl uses WARM to discover her boyfriend two-timing. Suddenly, WARM becomes a tool to test lover’s loyalty. Whether this function is imaged by WARM team in the beginning or not. Mr. Wednesday thinks that everyone don’t want his/her personal information is open on the Web but the desire of peeking is always popular. This is a conflict situation, however, it may be a corner for social network search engine to live.

Although Web facilitates people to extend their friendships, is it makes people to get along with more friends? While the cost of making friends lowers, does it lower the value of friendships? Maybe, Web is the mirror of the real world and people on the Web are still get used to the groups which they are familiar with in the real world.

Related Readings: (Chinese)

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