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- ZeitschriftenartikelInterview with Bernado A. Huberman on Social Media and Collective Intelligence(KI - Künstliche Intelligenz: Vol. 27, No. 1, 2013) Metaxas, Panagiotis Takis
- ZeitschriftenartikelCrowd-Powered Systems(KI - Künstliche Intelligenz: Vol. 27, No. 1, 2013) Bernstein, Michael S.Crowd-powered systems combine computation with human intelligence, drawn from large groups of people connecting and coordinating online. These hybrid systems enable applications and experiences that neither crowds nor computation could support alone.Unfortunately, crowd work is error-prone and slow, making it difficult to incorporate crowds as first-order building blocks in software. We introduce computational techniques that decompose complex tasks into simpler, verifiable steps to improve quality, and optimize work to return results in seconds. Using these techniques, we prototype a set of interactive crowd-powered systems. The first, Soylent, is a word processor that uses paid micro-contributions to aid writing tasks such as text shortening and proofreading. Using Soylent is like having access to an entire editorial staff as you write. The second system, Adrenaline, is a camera that uses crowds to help amateur photographers capture the exact right moment for a photo. It finds the best smile and catches subjects in mid-air jumps, all in realtime. These systems point to a future where social and crowd intelligence are central elements of interaction, software, and computation.
- ZeitschriftenartikelEditorial(KI - Künstliche Intelligenz: Vol. 27, No. 1, 2013) Klügl, F.
- ZeitschriftenartikelA Brief Tutorial on How to Extract Information from User-Generated Content (UGC)(KI - Künstliche Intelligenz: Vol. 27, No. 1, 2013) Egger, Marc; Lang, AndréIn this brief tutorial, we provide an overview of investigating text-based user-generated content for information that is relevant in the corporate context. We structure the overall process along three stages: collection, analysis, and visualization. Corresponding to the stages we outline challenges and basic techniques to extract information of different levels of granularity.
- ZeitschriftenartikelSocial Media and Collective Intelligence—Ongoing and Future Research Streams(KI - Künstliche Intelligenz: Vol. 27, No. 1, 2013) Schoder, Detlef; Gloor, Peter A.; Metaxas, Panagiotis TakisThe tremendous growth in the use of Social Media has led to radical paradigm shifts in the ways we communicate, collaborate, consume, and create information. Our focus in this special issue is on the reciprocal interplay of Social Media and Collective Intelligence. We therefore discuss constituting attributes of Social Media and Collective Intelligence, and we structure the rapidly growing body of literature including adjacent research streams such as social network analysis, Web Science, and computational social science. We conclude by making propositions for future research where in particular the disciplines of artificial intelligence, computer science, and information systems can substantially contribute to the interdisciplinary academic discourse.
- ZeitschriftenartikelInterview with Jim Hendler on Social Media and Collective Intelligence(KI - Künstliche Intelligenz: Vol. 27, No. 1, 2013) Metaxas, Panagiotis Takis
- ZeitschriftenartikelNews(KI - Künstliche Intelligenz: Vol. 27, No. 1, 2013)
- ZeitschriftenartikelSpecial Issue on Social Media(KI - Künstliche Intelligenz: Vol. 27, No. 1, 2013) Schoder, Detlef; Gloor, Peter A.; Metaxas, Panagiotis Takis
- ZeitschriftenartikelCan Computers Learn from the Aesthetic Wisdom of the Crowd?(KI - Künstliche Intelligenz: Vol. 27, No. 1, 2013) Bauckhage, Christian; Kersting, KristianThe social media revolution has led to an abundance of image and video data on the Internet. Since this data is typically annotated, rated, or commented upon by large communities, it provides new opportunities and challenges for computer vision. Social networking and content sharing sites seem to hold the key to the integration of context and semantics into image analysis. In this paper, we explore the use of social media in this regard. We present empirical results obtained on a set of 127,593 images with 3,741,176 tag assignments that were harvested from Flickr, a photo sharing site. We report on how users tag and rate photos and present an approach towards automatically recognizing the aesthetic appeal of images using confidence-based classifiers to alleviate effects due to ambiguously labeled data. Our results indicate that user generated content allows for learning about aesthetic appeal. In particular, established low-level image features seem to enable the recognition of beauty. A reliable recognition of unseemliness, on the other hand, appears to require more elaborate high-level analysis.
- ZeitschriftenartikelFrom Texts to Networks: Detecting and Managing the Impact of Methodological Choices for Extracting Network Data from Text Data(KI - Künstliche Intelligenz: Vol. 27, No. 1, 2013) Diesner, JanaThis thesis (Diesner in Technical Report CMU-ISR-12-101, 2012) addresses a series of methodological problems related to extracting information on socio-technical networks from natural language text data. Theories and models from the social sciences are leveraged and combined with computational approaches to (a) construct, analyze and compare network data and (b) combine text data and network data for analysis. This thesis entails various projects that serve three purposes: First, the impact of various common coding choices, including reference resolution and co-occurrence-based link formation, on network data and analysis results is empirically identified across multiple types of text data and domains. Second, different relation extraction methods are compared across various over-time, open-source, large-scale datasets with respect to the resulting network data and analysis results. This study offers a complement to traditional strategies for accuracy assessment. The relation extraction methods considered include network data construction based on (a) manually versus automatically built thesauri, (b) meta-data, and (c) collaboration with subject matter experts. Third, the concepts of grouping and roles from network analysis are integrated with text mining methods to enable the theoretically grounded, joint consideration of text data and network data for real-world applications.Overall, in this thesis, an interdisciplinary and computationally rigorous approach is used; thereby advancing the intersection of network analysis, natural language processing and computing. The contributions made with this work help people to utilize text data for network analysis, and to collect, manage and interpret rich network data at any scale. These steps are preconditions for asking substantive and graph-theoretic questions, testing hypotheses, and advancing theories about networks.