In ELWMS.KOM, learners are collecting web resources that meet their specific information needs. How- ever, not all web resources are homogeneous: different applications have different paradigms and conventions of usage, content creation, authorship, annotation (e.g. commenting) and reception, and therefore provide different kinds of information in different ways. Web resources can be grouped into self–similar classes (subsequently called web genres) based on these properties. For example, web re- sources have diverse functionalities (and thus can be assigned different genres): displaying a product (e.g. the web genre e–shop), representing a person in a social or organizational context (e.g. personal or
academic homepage) or allowing to follow and take part in discussions in a forum.
In ELWMS.KOM, the Type tag type has been introduced in order to capture this diversity of different web genres. As, section 3.1.2 shows, in ELWMS.KOM, the multilingual nature of LRs is an additional challenge. Thus, an approach that automatically determines the web genre of a web resource should be able to handle resources in different languages and therefore do not depend on linguistic features of a resource.
5.1.1 Examination of Tags denoting Web Genres in Social Bookmarking
The differentiation between different text and web genres is valuable for personal organization of knowl- edge. For example, genre information is commonly utilized in tagging applications, and Golder and Huberman [81] name genre tags among several different other utilization possibilities of tags:
Topic tags represent the overwhelming majority of tags used in tagging systems, as they identify what (or who) a resource is about.
Genre tags identify the kind of resource in addition to the topic. For example, web resources can be tagged as example, article or as a web genre like blog or wiki.
Content ownership tags represent the entity that owns or created the tagged resource, e.g. W3C for a web resource containing the W3C HTML 4.0 standard.
Tags identifying qualities or characteristics often denote subjective opinions about the tagged resource, e.g. cool or funny.
Refining tags do not stand alone but refine other tags, often numbers or years are used, e.g. 2010. Self–reference tags are used to identify content in terms of its relation to the tagger, e.g. mystuff. Task organization tags serve to group resources needed in a related task and structure the task of a
tagger, e.g. toread or jobsearch.
Respectively tags that denote topics of resources and tags that encode the genre of resources are often used. The latter contain — to a high degree — different text and web genres. This can be observed in different tagging applications, especially in social bookmarking applications. For example, in a subset of bookmarks of Delicious (crawled in March 2008, containing approximately 22 million bookmarks tagged with 64 million tags of over 41,000 users — that is about 527 resources per user and 1.2 million unique tags), the web genres blog, wiki and forum occur in the top 200 tags. Especially the tag blog (including its synonyms blogs, weblog and weblogs) makes up 14.01%0 of all tags, making it the tag with the
highest frequency. Also, for example, the web genres wiki (1.90%0) and forum (0.98%0) are frequently
used. Further, text genres are equally important, e.g. the tags reference (8.21%0) and howto (5.77%0)
are the most important for denoting text genre. Thus, a substantial part of tags used for tagging web resources represents text or web genres. The 50 tags with the highest frequency are listed in table C.3 in
appendix C. For comparison, figure 5.3 shows that the most current popular tags used on Delicious have not changed substantially since 2008, only their ranking order has changed slightly.
Figure 5.3:Tag cloud of the most often used tags of Delicious as of 2011-01-17 according to http://www. delicious.com/popular/
However, users often tag inconsistently [124, 81, 36]. For example, this can be observed with different tags (e.g. blog, blogs, weblog and weblogs) denoting the same web genre. Thus, automatically detect- ing genre tags for web resources is helpful, as it standardizes the tag names and enriches the metadata used for describing web resources.
For supporting the user to attach consistent tags to web resources, many approaches have been pre- sented that automatically extract topical tags from web resources and recommend these to the user (e.g. [39, 52, 133, 180]), often taking into account personal tagging behaviour or an existing folksonomy. However, detecting the web genre of a web resource in contrast to topical tags is not possible based on content words or already assigned tags exclusively. Thus, a different approach has to be followed.
5.1.2 Other Scenarios for Web Genre Detection
Besides tagging, typical use cases for automatic detection of web genres can be found in all fields where analysing huge amounts of unstructured information from the web is involved: for example, in the field of IR, users searching for information on the web may get more specific search results, if they are able to state what kind of information in what type of page they expect as a result [60]. Further, web pages contain more than the pure information to be extracted, e.g. usually there are navigation elements, headers or footers. Because this adds so–called noise to the information space that is analysed and searched [201], knowing the genre of a web resource enables to apply specifically tailored pre– processing steps, thus reducing noise effectively.
Another usage scenario for web genre metadata is Community Mining, i.e. analysing network struc- tures of communities of users in social software applications and extracting properties and the structure thereof [43]. A popular approach to Community Mining is crawling the web pages of one or multiple social software applications (e.g. a wiki or a subset of the blogosphere) by following hyperlinks to other web pages and extracting the desired information. Here it is important to know the genre of the linked web resources in order to use heuristics tailored to the according web genre. For example, a typical wiki
article has several authors that edit the same content, whereas a blog post normally has only one single author, thus authorship of content has to be identified in different ways.
5.1.3 Structure of this Chapter
In this chapter, the challenge of recognizing and distinguishing the web genres wikis, forums and blogs is targeted, as these are content–creation backbones of online communities, widely adopted and used, and support different paradigms of content creation and collaboration. Further, as they often contain informative content, web resources of these genres are often bookmarked for later reference, especially in self–directed learning settings. The contributions of this chapter are as follows: section 5.2 presents an overview of other approaches to recognize the web genre of a given page. The pattern features for each web genre to classify are derived in section 5.3 and novel pattern features are proposed that do not depend on a linguistic analysis of the web page’s content but rather analyse the structure of the web resource’s HTML markup. Thus, this novel approach called LIGD is fully language agnostic and inde- pendent of specific language analysis tools (e.g. part–of–speech taggers). Further, section 5.4 presents a corpus that reflects the choice of web genres, including resources in different languages and provided by different applications. In section 5.5 an evaluation of LIGD is given that shows that this approach performs well with94.3% sample pages correctly classified. Finally, section 5.6 gives a conclusion and presents perspectives for future work.
5.2 Related Work
Genre is a term widely used in rhetoric, literary theory, media theory and linguistics to refer to a dis-
tinctive type of work (particularly texts) [50]. Genre Theory provides a framework for classifying and grouping these works into well–defined taxonomy schemes.
As Kessler et al. [101] describe the term genre in the context of texts, it “is necessarily a heterogeneous classificatory principle, which is based among other things on the way a text was created, the way it is distributed, the register of language it uses, and the kind of audience it is addressed to”.
Building on Kessler’s terminology, the term web genre is used to describe and classify web resources by structural, functional, contextual and institutional characteristics like style, form, content and use of language. It spans use, intention and display of web resources and is considered orthogonal to the topic of a web resource [27, 26].
In contrast to web genre classification, web page classification ([190, 163]) takes into account a topical classification taxonomy scheme. For example, web page classification assigns web resources to different topics like “Family” or “Garden” [199], whereas web genre classification assigns them to functional classes like “e–Shop” or “personal homepage” [132].
5.2.1 Ambiguity of Taxonomies and Evolution of Web Genres
Chandler [50] notes that classifying genres into a hierarchical genre taxonomy is not a neutral and ob- jective procedure. This means that there are no “right” or “wrong” genre taxonomies, their use depends on the point of view of the researcher.
Depending on the anticipated use case, coarse or fine–grained categories for genres are developed (e.g. blog / wiki versus personal / academic homepage) that base on different properties of a web resource. However, even for human beings, recognizing and classifying web genres is not easy and heavily depends
on unambiguity of said categories and planned use [132]. For example, how would a bliki (a hybrid genre form of blogs and wikis) be categorized if only blogs or wikis were available as classes?
Further, although genres follow certain conventions and quasi–standards internally, they may develop aspects that are different between web resources of the same genre [27, 168]. For example, a particularly influential factor is time: genres evolve, their presentational and structural properties follow a possibly transformed use case and new trends appear (especially regarding technology). This affects web genres in particular: for example, blogs first were used as a platform to publish informal articles to a small circle of friends and family, similar to a personal diary. With the advancement of the Web 2.0 hype, content publishers like newspapers and corporations started to use the medium blog as the means to easily distribute their contents, the blog entries being written in more formal style of language. Thus, the style of writing has changed considerably over time, making it difficult for automatic text genre detection to derive the correct genre. Further, complex web pages used to be based on table layout. Nowadays, however, the paradigm of semantic HTML gains more and more importance and acceptance of new technologies, e.g. support of CSS2 in major web browsers, enables authors of web resources to layout web pages by other means than tables, affecting the presentational properties of pages with the same content now and then considerably. Eventually, since it has become possible to embed content from other third parties like comments1, the structure of the web genre blog has changed substantially, as it is not necessary to provide an own comment system anymore. This lack of a comment system changes the layout of the blog page. Thus, all approaches to detect the genre of a web resource based on its structure are only valid while the underlying genre itself does not evolve too significantly.
5.2.2 Related Approaches
Related approaches differ in web genres taken into account, features (also named cues by some re- searchers) used for classifying the web genres and machine learning algorithms they use for the actual classification task.
Detecting a genre of electronic texts has been a focus of research since the late 1980s (cf. [101]). These approaches mainly focused on plain text, thus they primarily applied linguistic analysis and some structural metrics (punctuation, sentence–length, readability metrics like the Flesch metric [73]) in or- der to identify the genre [60, 71, 72]. In contrast to text genre identification, web genre identification additionally may take into account structural features provided by the markup of HTML–based web resources.
In a user study, Meyer zu Eissen et al. [132] identify seven web genres that are relevant for users’ expectations towards the genres of search results when looking for information using search engines. They conclude with the following web genres fulfilling the users’ expectations: Help (e.g. Frequently Asked Questions (FAQs)), Article (e.g. scientific articles, but they also subsume all longer web pages with continuous text), Discussion (forums, mailing lists), E–shop (product presentations and sale), Non–private
Portrayal (presentation of enterprises and public institutions), Private Portrayal (personal homepages), Link Collections (documents that largely consist of links) and Download Pages (pages that offer software
to be downloaded). The authors consider use cases that require classification in real–time (online), so they state that the features used for classifying must not be too complex or computationally expensive to collect. They differentiate between three groups of features: document terms (e.g. frequencies of words, number of spelling errors, closed class word sets, i.e. typical keywords for each genre), linguistic features like part–of–speech (POS) analysis and syntactical analysis and simple text statistics (e.g. frequencies 1 e.g. Disqus (http://disqus.com/, retrieved 2011-02-07) provides external hosting of comments
of punctuation). Based on these features they develop two feature sets A and B that can be primarily discerned by computational costs: feature set A is based on analysis of certain markup elements, simple text statistics and genre specific closed class word sets. Feature set B is based on POS and linguistic analysis. Multi–Layer Perceptron neural networks and Support Vector Machines (SVMs) are used to actually classify the genres and the results range between 60% and 80% accuracy with feature set B slightly outperforming the computationally inexpensive feature set A.
Dewdney et al. [60] focus on the web genres Advertising, Bulletin Board, FAQ, Message Board, Ra-
dio News, Television News and Reuter Newswire. They compare the use of presentation features, word
features, as well as the combination of using Naive Bayes, C4.5 decision trees and SVM classifiers. Pre- sentational features reflect the way information is presented. Relevant word features are individual words weighted by tf–idf (term frequency — inverse document frequency), a statistical measure used to evaluate how important a word is for a document in relation to its occurrence in a collection or corpus. Then the word features are filtered by estimated Information Gain (a method to reduce compu- tational complexity in machine learning by reducing irrelevant features). Here, Dewdney et al. subsume linguistic features (e.g. use of tense, prevalence of adjectives as detected by POS tagging), layout fea- tures (e.g. line–spacing and non–alphanumeric characters) and miscellaneous features (e.g. readability measures, average length and sentence length and punctuation). For the seven mentioned genres they experimentally gain92% classification accuracy.
Amitay et al. [2] not only classify genres of web resources but also genres of whole sites, like Enterprise
Web Sites, Media Sites (e.g. sites of major TV stations and newspapers), E–shops and Universities. They
use structural features that result from site and link structure on several web pages of the site only. Ingoing links, internal links and outgoing links are put into relation to the same page, the same site and external resources on the web. The performance of this approach is satisfactory with about 60% correct classifications. This allows to draw the conclusion that classification of genres of whole websites is possible by only taking into account the link structure between pages.
A comprehensive compilation of web genres is found in the works of Santini [167, 168, 169]. She presents a selection of seven representative web genres: blog, e–shop, FAQs, online front–page (e.g. main pages of universities, enterprises and public organizations), listings, personal homepage and search en-
gine pages. Santini discerns — along the lines of Meyer zu Eissen and Stein [132] — three different sets
of features that she uses for classification: linguistic facets (similar to part–of–speech word sets), word frequencies (based on bag–of–word sets) and likewise structural information in HTML markup. The features deemed as most effective are closed class word sets and markup information (e.g. size of page in characters, number and frequencies of HTML tags and internal / external navigability through hyper- links). Besides these, other features like POS trigram frequencies, HTML facets and several linguistic facets are evaluated. A concise summary is available online at the author’s web page2. In [169], Santini proposes a multi–classification approach, as she states that often a single genre is not enough when there are ambiguous genre classes. Additionally, as genres are evolving, she postulates to follow a more flexi- ble, dynamic approach of genre classification, allowing the assignment of none, one or multiple genres to a web resource.
Levering et al. [118] investigate whether visual features of HTML web pages can improve the classi- fication of fine-grained genres instead of focusing on text–based features. They purposely choose their genres (store homepages, store product lists and store product descriptions) to be easily distinguishable in order to focus on feature construction. Besides textual features (readability measures, bag–of–words, POS and text statistics) and HTML features (link, form and HTML tag counts, scripts and URL features) 2 http://www.itri.brighton.ac.uk/~Marina.Santini/, retrieved 2008-10-21, see section “Genre Features”
they heavily apply visual features like image counts and statistics (e.g. average of all image sizes), area statistics (e.g. total areas of different object types, their relative percentages in comparison to the total area of the object type) and placement statistics (e.g. location distribution of terms). Their use of all these (over 12,000) features results in classification accuracies between85% and 95% using SVM.
5.2.3 Approaches to Classifying Blogs
For the approach presented in this thesis, LIGD, the following publications are relevant as they focus on classification of a single genre (here: blogs) and have a similar understanding of web genres as presented in this chapter.
Nanno et al. [142] focus on recognizing Japanese blogs and blog–alikes. Their goal is to identify blogs, i.e. classifying into blog or not a blog. Their approach is based on the observation that one of the most significant properties of a blog is that blog systems usually present their posts in temporal linearity, i.e. that dates and times that are presented are either in strictly ascending or descending order. Thus, consistently formatted date strings serve as features for recognizing blogs as well as delimiters for detecting single blog posts. Instead of machine learning, Nanno et al. use manually created patterns for the identification of these date strings and genre determination, claiming accurate classification in84% of all cases.
Elgersma et al. [65] focus on markup based features (e.g. number of HTML comments) and closed class word sets like “archive” or “comment” in order to recognize blog entries on arbitrary web pages. Classification is binary, i.e. only the fact whether a given page is a blog or not is recognized. Further- more their algorithm tests given pages on occurrence of links to some of the 20 most–used blog hosting providers (which is arguably not a very stable feature, as providers come and go). On these rather simple features different techniques and machine–learning algorithms are executed. According to the results, Elgersma et al. propose using Support–Vector–based methods for comparable classification tasks (e.g. SVM, with approximately93% accuracy), although there is no significant difference between most of the other 17 learning algorithms which have been applied (between88% and 93% accuracy).
Kolari et al. [106] mention that blogs as a publishing mechanism have crossed international boundaries and therefore blog posts are often written in another language than English. They even report blogs that are multilingual, i.e. blog posts of the same blog are written in different languages. Therefore they introduce an additional feature called bag–of–n-grams besides bag–of–words that converts text into tokens of characters with a certain window length. This enables their approach to take into account the often similar word stems in different related languages (e.g. the English comment and the German Kommentar still have the four–gram ment in common and thus may provide further cues on classification). However, they do not mention how good this approach is with respect to detecting the genre of multilingual web resources.