For her M.Sc. Project, conducted at the Netherlands Institute for Sound and Vision (NISV), Information Sciences student Anggarda Prameswari (pictured right) investigated a local crowdsourcing application to allow NISV to gather crowd annotations for archival audio content. Crowdsourcing and other human computation techniques have proven their use for collecting large numbers of annotations, including in the domain of cultural heritage. Most of the time, crowdsourcing campaigns are done through online tools. Local crowdsourcing is a variant where annotation activities are based on specific locations related to the task.
Anggarda, in collaboration with NISV’s Themistoklis Karavellas, developed a platform called “Elevator Annotator”, to be used on-site. The platform is designed as a standalone Raspberry Pi-powered box which can be placed in an on-site elevator for example. It features a speech recognition software and a button-based UI to communicate with participants (see video below).
The effectiveness of the platform was evaluated in two different locations (at NISV and at Vrije Universiteit) and with two different modes of interaction (voice input and button-based input) through a local crowdsourcing experiment. In this experiments, elevator-travellers were asked to participate in an experiment. Agreeing participants were then played a short sound clip from the collection to be annotated and asked to identify a musical instrument.
The results show that this approach is able to achieve annotations with reasonable accuracy, with up to 4 annotations per hour. Given that these results were acquired from one elevator, this new form of crowdsourcing can be a promising method of eliciting annotations from on-site participants.
Furthermore, a significant difference was found between participants from the two locations. This indicates that indeed, it makes sense to think about localized versions of on-site crowdsourcing.
[This post describes Aschwin Stacia‘s MSc. project and is based on his thesis]
There are many online and private film collections that lack structured annotations to facilitate retrieval. In his Master project work, Aschwin Stacia explored the effectiveness of a crowd-and nichesourced film tagging platform, around a subset of the Eye Open Beelden film collection.
Specifically, the project aimed at soliciting annotations appropriate for various types of media scholars who each have their own information needs. Based on previous research and interviews, a framework categorizing these needs was developed. Based on this framework a data model was developed that matches the needs for provenance and trust of user-provided metadata.
A crowdsourcing and retrieval platform (FilmTagging) was developed based on this framework and data model. The frontend of the platform allows users to self-declare knowledge levels in different aspects of film and also annotate (describe) films. They can also use the provided tags and provenance information for retrieval and extract this data from the platform.
To test the effectiveness of platform Aschwin conducted an experiment in which 37 participants used the platform to make annotations (in total, 319 such annotations were made). The figure below shows the average self-reported knowledge levels.
The annotations and the platform were then positively evaluated by media scholars as it could provide them with annotations that directly lead to film fragments that are useful for their research activities.
Nevertheless, capturing every scholar’s specific information needs is hard since the needs vary heavily depending on the research questions these scholars have.
[This post was written by Marc Jacobs and describes his MSc Thesis research]
Nowadays the world does not just rely on traditional news sources like newspapers, television and radio anymore. Social Media, such as Twitter, are claiming their key position here, thanks to the fast publishing speed and large amount of items. As one may suspect, the credibility of this unrated news becomes questionable. My Master thesis focuses on determining measurable features (such as retweets, likes or number of Wikipedia entities) in newsworthy tweets and online news articles.
The gathering of the credibility features consisted of two parts: a theoretical and practical part. First, a theoretical credibility framework has been built using recent studies about credibility on the Web. Next, Ubuntu was booted, Python was started, and news articles and tweets, including metadata, were mined. The news items have been analysed, and, based on the credibility framework, features were extracted. Additional information retrieval techniques (website scraping, regular expressions, NLTK, IR-API’s) were used to extract additional features, so the coverage of the credibility framework was extended.
The last step in this research was to present the features to the crowd in an experimental design, using the crowdsourcing platform Crowdflower. The correlation between a specific feature and the credibility of the tweet or news article has been calculated. The results have been compared to find the differences and similarities between tweets and articles.
The highly correlated credibility features (which include the amount of matches with Wikipedia entries) may be used in the future for the construction of credibility algorithms that automatically assess the credibility of newsworthy tweets or news articles, and, hopefully, adds support to filter reliable news from the impenetrable pile of data on the Internet.