Text in social networking Web sites: A word frequency analysis of Live Spaces
First Monday

Text in social networking Web sites: A word frequency analysis of Live Spaces
by Mike Thelwall

 

Abstract
Social networking sites are owned by a wide section of society and seem to dominate Web usage. Despite much research into this phenomenon, little systematic data is available. This article partially fills this gap with a pilot text analysis of one social networking site, Live Spaces. The text in 3,071 English–language Live Spaces sites was monitored daily for six months and word frequency statistics calculated and compared with those from the British National Corpus. The results confirmed the existence of common domain–specific words and a marked personal focus. Unexpectedly, however, there was no evidence of an unusual degree of experimentation with new words or word spellings — perhaps this behaviour is limited to other social networking environments. Also surprising was the existence of a marked male gender bias in the most commonly used words. This was probably caused by a significant number of news–related discussions involving predominantly male politicians and other male public figures.

Contents

Introduction
Literature review
Research hypotheses
Data
Results and analysis
Conclusions

 


 

Introduction

Social networking spaces are Web sites that allow members to create their own personal Web profile and to discover and connect to other members through that profile. One example is MySpace.com, which apparently surpassed Google as the most visited Web site by U.S. users at the end of 2006 (Prescott, 2007). MySpace is one of the more informal social networking sites, with users who are primarily young, and with profiles typically including a ‘cool’ personal photograph, a pop song, a set of photographs of friends and funny messages from some of those friends (and pop groups). Other popular social networking sites include Facebook (emphasising students), LinkedIn (for business networking) and Digg (for news discovery).

This article focuses on general social networking sites like MySpace and Live Spaces. These are linguistically interesting because they encourage new forms of communication. In fact, there are multiple communication modes, particularly between people who are connected as friends within a site. These modes include the following:

  • Friend messages. These are effectively e–mail messages sent to registered friends and typically use an interface that encourages short messages.
  • Wall postings, friend comments or testimonials. These are public messages placed on a friend’s Web profile page. This form of posting was apparently invented by early Friendster users (boyd, 2007a).
  • Comments are publicly visible messages attached to a picture or video on a friend’s site, although this terminology is also used for wall postings.

In addition to the above, there are one–to–many communication forms such as blog postings, personal statements and biographical details on the profile page and descriptions of any videos or pictures posted. Thus, social networking sites are a complex multimedia, multi–modal communication environment, designed to facilitate interactions amongst friends and which presumably generate a range of evolving communication registers that combine aspects of text messaging, e–mail and spoken styles.

It is important to research social networking language, not just as a widespread aspect of culture, but also to be able to teach it to English learners and to support and understand its use amongst children and young adults. Linguistic insights may also help to address current concerns about the amount of time that children spend online interacting with friends. This article focuses on one aspect of social network communication: one–to–many messages in blogs or photo sets. This restricted choice was made for technical reasons, described below. The British National Corpus was selected as a baseline for comparison and Microsoft Live Spaces was chosen as the social networking site, again for technical reasons described below. The research question is explanatory: Which are the main ways that Windows Live Spaces blogging and photograph comments differ from standard and Web English, from a word frequency perspective?

 

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Literature review

This research applies techniques from corpus linguistics to social networking, perhaps for the first time. Hence this literature review covers both content: social networking research; and methods: corpus linguistics.

Social networking

Research into social networks so far has tended to be qualitative, using ethnographic or similar approaches to gain insights into the culture of social networking, particularly with regard to teenage users. This research has been particularly useful for the way that it has addressed concerns raised by those in the media and elsewhere that have made false interpretations through superficial readings of the technology. The key concept of ‘friend’ for example, does not match well with its widely used offline meaning. Users of social networking sites typically become friends by one sending a friend request and the other accepting it. Friendship confers to a set of rights, such as permission to view a full profile, to send a message, to post comments and to view photographs and blog entries. A user’s friends are normally listed on their home page, or on additional pages if there are too many.

The various social networking sites all have their own unique features or orientations. For example, MySpace emphasises music, Facebook originated in U.S. colleges, blackplanet.com targets a black U.S. audience and gaiaonline.com focuses on manga and role–play. As such, it would be reasonable to expect significantly different user demographics. For example, it seems that Facebook users tend to be richer and better educated than MySpace members (boyd, 2007b). This and the tendency for social networking behaviour to evolve over time and for users to flock to different sites means that it is difficult to give general and definitive statements about social networking. Nevertheless, some research has yielded useful insights.

Users view friendship in different ways, ranging from equivalent to offline friendship to a totally meaningless relationship (boyd, 2006; Fono and Raynes–Goldie, 2006). This can cause conflict when two friends have different interpretations and act within their own understandings. In such a situation one may be seen as betraying the friendship bond whilst the other is “taking things too seriously”. Nevertheless, it seems to be the case that most social networkers view friending as weaker than offline friendship, for example accepting friendship requests to avoid giving offence (boyd, 2006). Moreover, within friendships there can be hierarchies: for example, a person’s closest friends could be listed on their home page with the remainder on other pages (boyd, 2006).

One of the few quantitative social network studies is a large–scale analysis of Facebook traffic (Golder, et al., 2007). This showed that most Facebook friends were members of the same college and also that most messages were exchanged between people sharing a college. It seems that Facebook use was built into the daily routines of students and Facebook messaging was often used as part of normal offline friendships. This might be seen as surprising since one of the advantages of social networking sites is their support for long distance and more casual friendships, for example, between former classmates attending different colleges.

Corpus Linguistics

The field of corpus linguistics (McEnery and Wilson, 2001) is concerned with the construction and analysis of large bodies of language (corpora). Perhaps the most famous is the British National Corpus (BNC), which consists of a wide variety of genres of English language text (Burnard, 1995). Although predominantly containing written text, from novels to newspapers, it also contains some transcribed spoken language. Language corpora are useful sources of usage evidence, particularly for language teaching (Jaworski, 1990). For example, a list of the most frequent words a representative corpus could form the starting point for a core vocabulary for second language teaching. Similarly, corpora can be queried for examples of text containing any word (concordances) so that students can identify appropriate use contexts. A drawback of corpora, however, is that they can take a long time to construct and can continue in use after some of their language is obsolete. A partial solution to this problem is to use the Web itself as a corpus, at least for concordances (Kilgarriff and Grefenstette, 2003; Meyer, et al., 2003).

A second type of corpus linguistics analysis, one that relates closely to the current paper, is comparative. If at least two corpora are available, or a corpus naturally divides into separate sections, then it may be useful to compare language use between them. For example, previous studies have compared nineteenth century letter writing styles by gender (Geisler, 2003), spoken against written English in universities (Biber, 2003) and Indian against British English (Hosali, 1991). The purpose of such investigations can be linguistic, in the sense of finding out how language works, or non–linguistic, for instance investigating gender divisions in society. Although there are many different ways by which the language can be compared across two corpora, one simple method is to compare word frequencies.

Word frequency analysis start by recording the number of times that each word occurs in the corpus. The distribution of these word frequencies is known to normally follow a Zipfian distribution (Zipf, 1949) or power law (Lotka, 1926; Thelwall, 2005a, 2005b) with a few words being very common and many words being rare. The classic shape of this distribution (see Figure 1) is consistent with a simple model of language use which posits that copying forms an important part of language. This implies that there is a positive feedback loop in language with people tending to use words that they have heard or read most often in the past. Comparing frequency distributions can be useful in two ways. First, differences in the rank order of the most common words illuminates stylistic differences. For example ‘I’ is more usual in spoken than written English, although it is common in both. Second, any deviations from a perfect power law distribution indicate some kind of external stress on the language. For example a corpus of legal documents may be “stressed” by the necessity to repeatedly use official legal jargon. Both of these methods are used in this article.

 

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Research hypotheses

The following four hypotheses drive this pilot study:

  1. Low frequency words will be unusually common in Live Spaces due to spelling mistakes and invented words.
  2. There will be a set of common domain–specific words related to Live Spaces features.
  3. In comparison to BNC English, text in Live Spaces will be more focused in the present and less in the past.
  4. In comparison to BNC English, text in Live Spaces will be more focused on the authors and their personal relationships.
  5. In comparison to BNC English, text in Live Spaces will be gender–neutral.

 

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Data

In order to obtain reliable word frequency statistics from social networking sites, a large number must be monitored for an extended period of time. Some sites have a feature that allows this monitoring to be conducted efficiently in terms of minimising the total download size needed. This feature is the Rich Site Summary/Really Simple Syndication (RSS) feed (Hammersley, 2005), which contains the most recently updated content on a site. In order to identify all of the content posted to a site it is sufficient to periodically check its RSS feed without having to repeatedly download and check the entire site.

Unfortunately there is no complete list of social networking sites from which a genuinely random sample could be selected for monitoring, instead search engine searches were used to create as large a list as possible of feeds from which to sample. For this, Windows Live searches were used because this search engine supports the ‘feed:’ command to identify feeds. We submitted 10,000 feed searches using random mid–frequency words from blogs. The combined results included over 100,000 unique URLs, with a majority coming from Windows Live Spaces. Windows Live Search seemed to have particularly good coverage of Live Spaces, perhaps even comprehensive coverage, and hence it was chosen as the sole social network site for this research. From the set of Live Spaces feeds we selected 26,953 at random (the strange number is due to rejecting inactive feeds). These were then monitored daily for six months using the Mozdeh RSS monitoring software. The feed data was parsed to remove all of the XML and HTML tags as well as all URLs. The English language feeds were identified through first separating out non–ASCII feeds and then using the bigram method of language identification on the remainder (Cavnar and Trenkle, 1994). This was chosen in preference to the common words method, which is less effective on small quantities of text (Grefenstette, 1995). The feeds were then manually checked, which removed about 200 incorrectly classified feeds and left 3,071 Live Spaces. A word frequency list was built from the tag–free text of these 3,071 Live Spaces.

For comparison purposes word frequency lists from the British National Corpus (BNC) and from U.K. university Web sites in 2003 were used from a previous article (Thelwall, 2005b). Neither of these are ideal as comparative corpora since Live Spaces members are presumably mainly from the U.S. but they form a useful baseline for broad comparisons.

 

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Results and analysis

Table 1 reports the most common words in the English Live Spaces feeds in addition to the same results for two comparison corpora. It is interesting that although the top word is the same for all three, there is already a difference in the second most common word, with ‘of’ being relatively rare in Live Spaces. There does not seem to be a plausible explanation for this result.

 

Table 1: The most common words in the English Live spaces feeds,
the BNC and U.K. university Web sites in 2003.
RankBNC UK Uni 2003Live Spaces (eng)
1thethethe
2ofofand
3andandto
4totoi
5aaa
6ininof
7isisin
8thatforit
9wasonthat
10itbeis
11forthatfor
12onthisyou
13withwithmy
14hebyphoto
15bearewas
16iasblogentry
17byiton
18asyouwith
19atorme
20youfrombe
21areathave
22hisanthis
23hadwillbut
24notnothe
25thishaveas
26havewhichwe
27fromuniversityat
28butiso
29whichifnot
30shecanphotoalbum
31theywasblog
32orallare
33anresearchall
34herinformationmore
35wereweentry
36therewasfrom
37weonethey
38theirmayone
39beenyouror
40hasbutby

 

Low frequency words

The first research question hypothesised that low frequency words would be unusually common in Live Spaces text. Figure 1 is a frequency distribution of word frequencies in Live Spaces. In terms of low frequency words, the graph has a very straight line towards the left without a notably high number of words with frequency 1. Hence, although words that occur only once in the corpus are more numerous than words that occur more often, they are not more frequent than would be expected by a language that follows a natural power–law growth model. In conclusion, there is no evidence of the deliberate construction of new words in Live Spaces postings. A manual inspection of words with frequency 1 supported this conclusion: these words seemed rarely to be invented words.

 

Figure 1: A frequency distribution of word frequencies in English Live Spaces.

Figure 1: A frequency distribution of word frequencies in English Live Spaces.

 

Domain–specific words

From Table 1 there are many domain–specific words in Live Spaces. Some of these are self-evidently domain–specific (blogentry, blog, photoalbum) and two more can be deduced to be domain–specific in that they relate to the contents of Live Spaces and are much more frequent than in the BNC or university corpora (photo, entry).

Temporal orientation

The occurrence of temporal participles gives a mixed pattern. The past participles ‘was’ (BNC rank 9; University rank 30; Live Spaces rank 15) and ‘had’ (BNC 23; university 136; Live Spaces 53) are rarer in Live Spaces than the BNC. Current participles ‘is’ (BNC 7; university 7; Live Spaces 10), ‘are’ (BNC 21; university 15; Live Spaces 32) and ‘have’ (BNC 26; university 25; Live Spaces 21) are more mixed: two of the three are rarer in Live Spaces. The future participle ‘will’ (BNC 41; university 23; Live Spaces 41) shows no difference. In conclusion, there is some evidence of less of an orientation in the past in Live Spaces in comparison to the BNC. This may be partly due to the incorporation of a body of narrative text in the BNC.

Live Spaces shows a pronounced orientation towards the writer in terms of the frequency of personal pronouns (high position for ‘I’: ‘me’ and ‘my’ missing from the BNC top 50). It also shows a wider tendency towards personal engagement in the higher position of the possessive pronouns ‘you’ and ‘your’ (although ‘your’ is higher in the university set).

Gender orientation

Although gender norms in offline and online communication are often more in terms of content and style than old–fashioned inappropriate use of gendered words (Herring, 2003; Livia, 2003), the gendered words in the corpus show a clear pattern. The masculine words ‘he’ (BNC 14; university 74; Live Spaces 24) and ‘his’ (BNC 22; university 100; Live Spaces 46) are relatively rare in Live Spaces. The feminine word ‘her’ (BNC 34; university 336; Live Spaces 63) and ‘she’ (BNC 30; university 381; Live Spaces 62) are also rarer in Live Spaces. More importantly, however, the feminine words are much rarer than the equivalent masculine words in Live Spaces, and so there is clear evidence of a gender bias towards male pronouns. An investigation into a random sample of occurrences of ‘he’ suggested that the main reason was the number of discussion of news or political events involving male politicians (e.g., George Bush) and other public figures. Presumably there is a predominance of men in the news. In addition, there were a number of other, apparently less common causes of gender bias:

  • Religious discussions, using ‘he’ for a God;
  • Some story–telling, using male main characters, including animals;
  • Hypothetical discussions of professional figures which are assumed to be male (e.g., what a lawyer would do in a divorce case); and,
  • Discussions of events involving male police officers (e.g., receiving a speeding fine).

In summary, the gender imbalance seems to stem mainly from society but also to some extent from gender–biased linguistic conventions (gods and professionals tend to be discussed as male).

 

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Conclusions

The results of the analysis supported two of the research hypotheses fully: the importance of domain–specific terms and the author/relationship focus of Live Spaces text. The results partially supported a hypothesis of a current temporal orientation in Live Spaces text: common past participles were rarer and future participles were equally as common but some present participles were more common and some less common.

Two hypotheses must be rejected. First, rare words, although common, were not more common than would be expected in a corpus following natural language laws. It seems that in Live Spaces text (mainly blogs and photojournals) there is little attempt to play with word creation and word spellings. It seems likely that a different result would have been obtained for other social network Web site text, such as MySpace comment sections. Hence it should not be assumed that all social networking text is equally innovative in its spelling and usage.

The second rejected hypothesis is the absence of a gender bias. Live Spaces text displayed a clear masculine orientation, although both masculine and feminine common words were less common than in the BNC. This bias seems to be predominantly due to discussions of news events, which are dominated by predominantly male–lead professions, such as politics. End of article

 

About the author

Mike Thelwall is Professor of Information Science in the School of Computing & Information Technology at the University of Wolverhampton.
Web: http://www.scit.wlv.ac.uk/~cm1993/mycv.html
E–mail: m [dot] thelwall [at] wlv [dot] ac [dot] uk

 

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Editorial history

Paper received 16 July 2007; accepted 15 January 2008.


Copyright © 2008, First Monday.

Copyright © 2008, Mike Thelwall.

Text in social networking Web sites: A word frequency analysis of Live Spaces
by Mike Thelwall
First Monday, Volume 13, Number 2 - 4 February 2008
http://www.firstmonday.org/ojs/index.php/fm/article/viewArticle/2117/1939





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