=Paper=
{{Paper
|id=None
|storemode=property
|title=Identifying Topic-Related Hyperlinks on Twitter
|pdfUrl=https://ceur-ws.org/Vol-1272/paper_101.pdf
|volume=Vol-1272
|dblpUrl=https://dblp.org/rec/conf/semweb/SiehndelKHR14
}}
==Identifying Topic-Related Hyperlinks on Twitter==
https://ceur-ws.org/Vol-1272/paper_101.pdf
Identifying Topic-Related Hyperlinks on Twitter
Patrick Siehndel, Ricardo Kawase, Eelco Herder and Thomas Risse
L3S Research Center, Leibniz University Hannover, Germany
{siehndel, kawase, herder, risse}@L3S.de
Abstract. The microblogging service Twitter has become one of the most popu-
lar sources of real time information. Every second, hundreds of URLs are posted
on Twitter. Due to the maximum tweet length of 140 characters, these URLs are
in most cases a shortened version of the original URLs. In contrast to the origi-
nal URLS, which usually provide some hints on the destination Web site and the
specific page, shortened links do not tell the users what to expect behind them.
These links might contain relevant information or news regarding a certain topic
of interest, but they might just as well be completely irrelevant, or even lead to a
malicious or harmful website. In this paper, we present our work towards iden-
tifying credible Twitter users for given topics. We achieve this by characterizing
the content of the posted URLs to further relate to the expertise of Twitter users.
1 Introduction
The microblogging service Twitter has become one of the most popular and most
dynamic social networks available on the Web, reaching almost 300 million active
users [1]. Due to its popularity and dynamics, Twitter has been topic of various ar-
eas of research. Twitter clearly trades content size for dynamics, which results in one
major challenge for researchers - tweets are too short to put them into context without
relating them to other information. Nevertheless, these short messages can be combined
to build a larger picture of a given user (user profiling) or a given topic. Additionally,
tweets may contain hyperlinks to external additional Web pages. In this case, these
linked Web pages can be used for enriching tweets with plenty of information.
An increasing number of users post URLs on a regular basis, and there are more
than 500 million Tweets every day1 . With such a high volume, it is unlikely that all
posted URLs link to relevant sources. Thus, measuring the quality of a link posted on
Twitter is an open question [3].
In many cases, a lot can be deduced just by the URL of a given Web page. For
example, if the URL domain is from a news provider, a video hosting website or a so-
cial network, the user already knows more or less what to expect after clicking on it.
However, regular URLs are, in many cases, too long to fit in a single tweet. Conse-
quently, Twitter automatically reduces the link length using shortening services. This
leads to the problem that the user’s educated guess of what is coming next is completely
gone. In this work, we focus on ameliorating these problems by identifying those tweets
containing URLs that might be relevant for the rest of the community.
The reasonable assumption behind our method is that users who usually talk about
a certain topic (‘experts’) will post interesting links about the same topic. The strong
1
https://blog.twitter.com/2013/new-tweets-per-second-record-and-how
point in our method is that it is independent of the users’ social graph. There is no need
to verify the user’s network or the retweet behavior. Thus, it can be calculated on the
fly. To achieve our final goal, we divide our work in two main steps: the generation of
user profiles [5] and the generation of URL profiles. In this paper, we focus on the latter
step.
2 Methodology
In our scenario, we build profiles for Twitter users based on the content of their tweets.
Besides the profiles for users we also generate profiles for the URLs posted by the users.
One of the biggest challenges in this task is to find appropriate algorithms and metrics
for building comparable profiles for users and websites. The method we developed to
solve this task is based on the vast amount of information provided by Wikipedia. We
use the articles and the related category information supplied by Wikipedia to define
the topic and the expertise level inherent in certain terms. Our method consists of three
main steps to create profiles for users and websites.
Extraction: In this step, we annotate the given content (all tweets of a user, or
the contents of a Web page) using the Wikipedia Miner Toolkit [4]. The tool provides
us with links to Wikipedia articles. The links discovered by Wikipedia Miner have a
similar style to the links that can be found inside a Wikipedia article. Not all words that
have a related article in Wikipedia are used as links, but only words that are relevant for
the whole topic are used as links, if a detected article is relevant for the whole text is
based on different features like the relatedness to other detected articles or generality of
the article.
Categorization: In the second stage, Categorization, we extract the categories of
each entity that has been mentioned in the users’ tweets or in the posted URL. For
each category, we follow the path through all parent categories, up to the root category.
In most cases, this procedure results in the assignment of several top-level categories
to an entity. Since the graph structure of Wikipedia contains also links to less relevant
categories, we only follow links to parent categories which distance to the root is shorter
or less than the one of the child category. For each category, a weight is calculated by
first defining a value for the detected entity. This value is based on the distance of
the entity to the root node. Following the parent categories, we divide the weight of
each node by the number of sibling categories. This step ensures, that categories do
not get higher values just because of a broader structure inside the graph. Based on this
calculation, we give higher scores to categories that are deeper inside the category graph
and more focused on one topic.
Aggregation: In the final stage, Aggregation, we perform a linear aggregation over
all of the scores for a document, in order to generate the final profile for the user (or
for the website). The generated profile displays the topics a user/website talks about, as
well as the expertise in - or focus on - a certain topic.
3 Validation
As mentioned in Section 1, in this paper we focus our attention on the generation of
URL profiles and the relation to the corresponding tweets and users. Thus, in order
to validate our methodology, we crawled Twitter with a number of predefined queries
(keywords) and collected all resulting tweets that additionally contain URLs. We have
Table 1. Statistics about the used dataset.
Items Annotations Annotations per Item
Topic Tweets 83,300 88,530 1.06
Linked Wedsites 40,940 457,164 11.1
All Tweets 11,303,580 30,059,981 3.127
Fig. 1. Coverage of Wikipedia Categories based on the URL Content for each selected topic.
previously validated our approach by characterizing and connecting heterogeneous re-
sources based on the aggregated topics [2]. Here, the goal is to qualitatively validate
if the topic assignment given by our method in fact represents the real topics that are
expected to be covered in a given query.
3.1 Dataset
The used dataset consists of around 83,300 tweets related to seven different topics.
The idea behind this approach is, to collect a series of tweets that contain links and
certain keywords relevant for one particular topic. Within these tweets, we found 40,940
different URLs. For each of these URLs, we tried to download and extract the textual
content, which resulted in 26,475 different websites. Additionally we downloaded the
last 200 posts for each user. The numbers of the dataset are shown in Table 1.
Table 2. Correlations between created profiles
URL Content URL Content Single Tweet
Single Tweet User Tweets User Tweets
Edward Snowden 0.995 0.968 0.961
Higgs Boson 0.812 0.628 0.496
Iphone 5 0.961 0.698 0.664
Israel Palastinian Talks 0.984 0.884 0.867
Nexus 5 0.968. 0.972 0.956
Obamacare 0.983 0.79 0.752
World Music Avards 0.921 0.718 0.614
All topics average 0.946 0.808 0.759
3.2 Topic Comparison
Figure 1 shows the generated profiles for two of the chosen example topics. The shown
profiles are averaged over all users and show the profiles based on the content of the
crawled web pages, based on the tweets containing the URLs and based on the complete
user profile (last 200 Tweets, based on API restrictions). We can see that for the very
specific topic ‘Israeli Palestinian Talks’ the generated profiles are very similar. For the
topic ‘iPhone 5’ the profiles are less similar, since this topic or keyword is less specific
it becomes much harder for a user to find the content he is looking for. A tweet like
‘The new iPhone is really cool’ together with a link may be related to many different
aspects of the product. Table 2 displays the correlation between the different profiles
for the chosen exemplifying topics. While users who write about topic like ‘Snowden’
or ‘Nexus phones’ seem to write about related topics in most of their tweets, this is not
true for more general topics.
4 Conclusion
In this paper, we presented a work towards the identification of credible topic-related
hyperlinks in social networks. Our basic assumption is that users who usually talk about
a certain topic (‘experts’) will post interesting (and safe) links about the same topic.
The final goal of our work requires to analyze the quality of the posted URLs. Here,
we presented our profiling method with preliminary results of the URL profiles. As
future work we plan to analyze the quality of profiles and URLs in order to provide a
confidence and quality score for URLs.
5 Acknowledgment
This work has been partially supported by the European Commission under ARCOMEM
(ICT 270239) and QualiMaster (ICT 619525)
References
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