\section{INTRODUCTION}

Keywords and phrases are in common use in information filtering 
(\cite{malone87,malone92,sheth93}),
but other criteria could be usable, especially in combination with
machine learning techniques. By studying how users of Usenet 
News select and rate information, we have searched for additional
filtering criteria.

\section{USER STUDIES}

%%- STUDIES OF USERS WAY OF SELECTING 
%%INFORMATION AND THEIR NEED OF 
%%INFORMATION FILTERING

In a first pilot study \cite{lantz93} the aim was to investigate how
experienced users of Usenet News selected their information and how
they reacted towards assisting the system in different ways i.\ e.\ by
rating contributions, write key words, use rules or by letting the
system take part of the users profile. Four subjects were instructed
to perform a set of tasks in Usenet News and think aloud during the
session. After the session the subjects were interviewed.

The results pointed at the fact that the users selected
their information by taking notice of the information in the message header;
mainly key words they saw in the title, and partly by browsing through
the text, where keywords and general layout are used for selection. In
an unfamiliar news group the selection took longer time, apparently
since the keywords were unfamiliar. The subjects were positive to
rating contributions, adding keywords and also to use rules.  On the
question if the system should be allowed to take part of the users
profile, one subject expressed the feeling of need for privacy
i.\e.\ not wanting the system to know all of the groups he was entering.

In a second pilot study \cite{lantz94} the aim was to investigate the
criteria for selecting information used by experienced readers of
Usenet News. Three subjects were instructed to select ten
contributions from five newsgroups each and to think aloud while they
read. They were asked to read the contributions and place each in one
of three categories (read now, save, discard).
%%after each contribution 
%%rate it according to a three-step scale.

%%The selections were done by reading or browsing through the texts. 
The subjects used depth of comment, keywords, patterns in the
contribution, amount of cited text, questions, length of text,
alphabetical signs or numbers to form their decicions. These criteria
have been devided into two groups; pattern recognitions and demands
reading.

A questionnaire have recently been distributed in a Swedish 
Usenet News newsgroup. The aim with the 
study was to answer the following questions:
\begin{itemize}
\item do users of Usenet News experience a need for filtering?
\item which criterias do the users adopt to find or avoid
information in Usenet News?  
\item in what way are the users willing to support the system?
\item how important do the users regard the information 
presented in Usenet News?
\item do the users experience that they have enough time to 
spend with the news system?
\end{itemize}

Twenty subjects were included in the study and the 
preliminary results show that:
\begin{itemize}
\item the need for filtering were experienced by 19 of the 
subjects and they did not express a need for filtering 
information in the meetings not subscribed.
\item criteria 
\end{itemize}

%%We hope that the results can help us to focus on the most 
%%relevant parts of the system while concerning the filtering 
%%mechanisms.

CONCLUDING REMARKS
