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Genius Media Group Incorporated is a collaborative annotation platform and was founded by Mahbod Moghadam, Tom Lehman and Ilan Zechory with the aim to annotate lyrics, which have no license and can be interpreted in form of in- line annotation by members. The first version was launched October 2009 as Rap Exegesis, then it changed to Rap Genius in December 2009 and finally in July 2014 the title Genius was given. Genius members have six different roles that are closely tied to authorizations sequentially: Whitehat, Editor, Moderator, Verified Artist, Mediator and Staff. We monitored Genius activities on firehose for five weeks, collected 1.3 million activities, 762 thousand of them are annotation activities1. We registered 57 thousand unique users and found, that users generate on average 13.33 annotation activities in this period of analysis, which is 0.36 annotation activities per day. The distribution over user groups displays the roles Moderator, Staff, Artist, Mediator and Whitehat. Whitehat embodies the most registered user, but when it comes to drive Genius ahead, then those roles are presented in the following sequence: Artist, Staff, Mediator, Moderator, Editor and at the end is the role Whitehat. Intelligence Quotient (IQ) can be earned by the most of activities and indicate experience of a member. Although a count of IQs is required to do certain activities, for instance to post into forums, but it does not promote automatically a member’s role to become a higher member level. High-quality annotations and decision maker such as an Editor establish nomination criteria. Earning IQs implies to edit pages; a page is edited on average 295 times, which varies greatly from the me- dian (195) times. This indicates that some pages attract users more than others. For developers Genius provides API, documentation and support forum as well as there are sub- domains in different countries and languages. We attempt to discover members' collaboration by editing Genius pages and for this purpose we clarify the social, technical and participation architecture of Genius, such as member’s permissions as well as options, activity types and distribution of page edits. The following technical report is structured as follows: Section 2 introduces the social structure of Genius, how to interact with the user interface, being a member and the relation between member roles, annotation and earned IQs. Section 3 continues with the technical structure, in which Genius subdomains are presented, what technical options are there for developers to bind Genius services in applications, firehose as notifications process as well as demographic trends of users at Genius. Section 4 describes our member activities study on Genius and which new findings we determined. Finally, section 5 presents our conclusions

Vertrauen generieren in kollaborativen Umgebungen

Trust is the key feature for human interaction, including the consumption of information. Due to the increasing distance communication in a digital world and the multiplicity of mostly unknown sources of information on the web, it has become di cult to identify trusted information. Increasing numbers of users consider online communities to be a source of information that can be created by almost any other user or more than one. However, one main challenge in such online communities is how to verify the credibility of this information. Furthermore, the various platforms and the complexity of the subject (trust) make the development of mechanisms to identify trustworthy information more challenging. More precisely, this raises the research question of what are the socio-technical design parameters for building trust in collaborative annotation environments? To this end, this dissertation has examined the collaborative environments Genius and Stackoverflow in light of their real data. The goal is to understand user behavior in order to identify the information characteristics that make such information trustworthy through interaction. This work proposes a trust model that comprises the dimensions stability, credibility, and quality. It calculates a trust degree of short-text based on its characteristics and classifies it into a trust class (very-trusted, trusted, untrusted and very-untrusted). The information characteristics were considered from two perspectives: Metadata and content. The evaluation of the metadata is based on user preferences within a survey, while the content is verified for its text-embedded features using data mining techniques. The proposed trust model supports the identification of trusted information in collaborative environments. It can be used in various online communities that deliver the appropriate metadata of the information provided. The trust model helps to filter the information and thus reduces the information-overload shared on the web. Applications can integrate the trust model into their development in order to increase the likelihood of their use, as users are able to recognize trusted information easily. In contrast to existing works, this thesis proposes a trust model that combines the metadata and short text characteristics to produce a human-readable interpretation of the calculated trust degree

Generating trust in collaborative environments: Evaluating design parameters in area of semantic annotations

Trust is the key feature for human interaction, including the consumption of information. Due to the increasing distance communication in a digital world and the multiplicity of mostly unknown sources of information on the web, it has become di cult to identify trusted information. Increasing numbers of users consider online communities to be a source of information that can be created by almost any other user or more than one. However, one main challenge in such online communities is how to verify the credibility of this information. Furthermore, the various platforms and the complexity of the subject (trust) make the development of mechanisms to identify trustworthy information more challenging. More precisely, this raises the research question of what are the socio-technical design parameters for building trust in collaborative annotation environments? To this end, this dissertation has examined the collaborative environments Genius and Stackoverflow in light of their real data. The goal is to understand user behavior in order to identify the information characteristics that make such information trustworthy through interaction. This work proposes a trust model that comprises the dimensions stability, credibility, and quality. It calculates a trust degree of short-text based on its characteristics and classifies it into a trust class (very-trusted, trusted, untrusted and very-untrusted). The information characteristics were considered from two perspectives: Metadata and content. The evaluation of the metadata is based on user preferences within a survey, while the content is verified for its text-embedded features using data mining techniques. The proposed trust model supports the identification of trusted information in collaborative environments. It can be used in various online communities that deliver the appropriate metadata of the information provided. The trust model helps to filter the information and thus reduces the information-overload shared on the web. Applications can integrate the trust model into their development in order to increase the likelihood of their use, as users are able to recognize trusted information easily. In contrast to existing works, this thesis proposes a trust model that combines the metadata and short text characteristics to produce a human-readable interpretation of the calculated trust degree

Investigating the effect of attributes on user trust in social media

One main challenge in social media is to identify trustworthy information. If we cannot recognize information as trustworthy, that information may become useless or be lost. Opposite, we could consume wrong or fake information with major consequences. How does a user handle the information provided before consuming it? Are the comments on a post, the author or votes essential for taking such a decision? Are these attributes considered together and which attribute is more important? To answer these questions, we developed a trust model to support knowledge sharing of user content in social media. This trust model is based on the dimensions of stability, quality, and credibility. Each dimension contains metrics (user role, user IQ, votes, etc.) that are important to the user based on data analysis. We present in this paper, an evaluation of the proposed trust model using conjoint analysis (CA) as an evaluation method. The results obtained from 348 responses, validate the trust model. A trust degree translator interprets the content as very trusted, trusted, untrusted, and very untrusted based on the calculated value of trust. Furthermore, the results show different importance for each dimension: stability 24%, credibility 35% and quality 41%

Ontology-based entity recognition and annotation

The majority of transmitted information consists of written text, either printed or electronically. Extraction of this information from digital resources requires the identification of important entities. While Named Entity Recognition (NER) is an important task for the extraction of factual information and the construction of knowledge graphs, other information such as terminological concepts and relations between entities are of similar importance in the context of knowledge engineering, knowledge base enhancement and semantic search. While the majority of approaches focusses on NER recognition in the context of the World-Wide-Web and thus needs to cover the broad range of common knowledge, we focus in the present work on the recognition of entities in highly specialized domains and describe our approach to ontology-based entity recognition and annotation (OER). Our approach, implemented as a first prototype, outperforms existing approaches in precision of extracted entities, especially in the recognition of compound terms such as German Federal Ministry of Education and Research and inflected terms

Calculating trust in domain analysis: Theoretical trust model

In recent decades, more information has become increasingly available on the Web. Every user can actively participate in the generation and exchange of information. Investigating the quality of user-generated content (UGC) has therefore become a necessity and an ever-increasing challenge. In collaborative environments where users collect, share and build a knowledge base, trust is an important factor. If, for example, we as users trust UGC on the Web, this influences our interaction with this content. The aim of our research is to propose a model for the evaluation of trust in UGC. Based on the available research results, we define a model for measuring trust in collaborative environments. Our approach is based on three dimensions: stability, credibility and quality. These three concerns are combined to create a trusted translator. We use a real-world data set of the social annotation platform Genius to calculate the value of our trust in an annotation. Based on this case study, we show which insights can be gained by calculating the trust in such an environment. When information has specific qualities, our approach will enable the user to better determine which information offers the highest level of trust