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SAM Tables of Contents: 14

Proceedings of the 2014 International Workshop on Socially-Aware Multimedia

Fullname:Proceedings of the 3rd International Workshop on Socially-Aware Multimedia
Editors:Pablo Cesar; David A. Shamma; Matthew Cooper; Aisling Kelliher
Location:Orlando, Florida
Standard No:ISBN: 978-1-4503-3124-1; ACM DL: Table of Contents; hcibib: SAM14
Links:Workshop Website | Conference Website
  1. Invited Talk
  2. Socially-Aware Multimedia
  3. Invited Talk

Invited Talk

Critically Aware Multimedia BIBAFull-Text 1
  Aisling Kelliher
Socially-aware multimedia research balances recognition of the import of human interactions with consideration for the media content itself. The goal of this form of mediated social communication work is both to achieve technical innovation and to provide a rich user experience. This motivation can be extended to encompass a broader exploration of the socio-cultural condition that uses the design and development of socially aware multimedia systems as a reflexive lens for examination and dialog. This form of critical technical inquiry moves beyond efficiency and user experience to more directly interrogate the values evident in the technologies themselves.
   A critical approach advocates deep reflection on structural norms with a target goal of affecting change. The concept of "Critical Technical Practice" is attributed first to Phil Agre, who initiated the idea based on his personal experience as an artificial intelligence researcher [1]. Chaffing at the perceived constraints of his field, Agre embraced critical theory and in particular the philosophy of phenomenology to more concretely examine and interpret his life research and work. Critical reflection allowed Agre to deeply consider the concepts, methods and modes of inquiry in AI, in a framing that has been adopted by practitioners and researchers in a variety of technical fields including computer science, engineering and human-computer-interaction [2].
   Much of this recent work owes an allegiance to the influence of the humanities and the arts. While we may be familiar with the role of critical theory in the contributions of philosophers and social scientists, we can also scan the artistic horizon for centuries of work integrating mediated reflection with acute social commentary. From Jonathan Swift to John Oliver, Laura Mulvey to Miranda July, the artist practitioner and commentator has functioned as a powerful conduit for provoking societal examination and dialog. Examining the impact of critical work across multiple disciplines which exposes both technical and social implications therefore presents considerable utility for social multimedia researchers. In the following sections, we present work bridging art, design, engineering and computer science as exemplars of work in this domain.

Socially-Aware Multimedia

Cyber Bullying Detection Using Social and Textual Analysis BIBAFull-Text 3-6
  Qianjia Huang; Vivek Kumar Singh; Pradeep Kumar Atrey
Cyber Bullying, which often has a deeply negative impact on the victim, has grown as a serious issue among adolescents. To understand the phenomenon of cyber bullying, experts in social science have focused on personality, social relationships and psychological factors involving both the bully and the victim. Recently computer science researchers have also come up with automated methods to identify cyber bullying messages by identifying bullying-related keywords in cyber conversations. However, the accuracy of these textual feature based methods remains limited. In this work, we investigate whether analyzing social network features can improve the accuracy of cyber bullying detection. By analyzing the social network structure between users and deriving features such as number of friends, network embeddedness, and relationship centrality, we find that the detection of cyber bullying can be significantly improved by integrating the textual features with social network features.
Recovering Social Interaction Spatial Structure from Multiple First-Person Views BIBAFull-Text 7-12
  Tian Gan; Yongkang Wong; Bappaditya Mandal; Vijay Chandrasekhar; Liyuan Li; Joo-Hwee Lim; Mohan S. Kankanhalli
In a typical multi-person social interaction, spatial information plays an important role in analyzing the structure of the social interaction. Previous studies, which analyze spatial structure of the social interaction using one or more third-person view cameras, suffer from the occlusion problem. With the increasing popularity of wearable computing devices, we are now able to obtain natural first-person observations with limited occlusion. However, such observations have a limited field of view, and can only capture a portion of the social interaction. To overcome the aforementioned limitation, we propose a search-based structure recovery method in a small group conversational social interaction scenario to reconstruct the social interaction structure from multiple first-person views, where each of them contributes to the multifaceted understanding of the social interaction. We first map each first-person view to a local coordinate system, then a set of constraints and spatial relationships are extracted from these local coordinate systems. Finally, the human spatial configuration is searched under the constraints to "best" match the extracted relationships. The proposed method is much simpler than full 3D reconstruction, and suffices for capturing the social interaction spatial structure. Experiments for both simulated and real-world data show the efficacy of the proposed method.
The Influence of Interactivity Patterns on the Quality of Experience in Multi-party Video-mediated Conversations under Symmetric Delay Conditions BIBAFull-Text 13-16
  Marwin Schmitt; Simon Gunkel; Pablo Cesar; Dick Bulterman
As commercial, off-the-shelf, services enable people to easily connect with friends and relatives, video-mediated communication is filtering into our daily activities. With the proliferation of broadband and powerful devices, multi-party gatherings are becoming a reality in home environments. With the technical infrastructure in place and has been accepted by a large user base, researchers and system designers are concentrating on understanding and optimizing the Quality of Experience (QoE) for participants. Theoretical foundations for QoE have identified three crucial factors for understanding the impact on the individual's perception: system, context, and user. While most of the current research tends to focus on the system factors (delay, bandwidth, resolution), in this paper we offer a more complete analysis that takes into consideration context and user factors. In particular, we investigate the influence of delay (constant system factor) in the QoE of multi-party conversations. Regarding the context, we extend the typical one-to-one condition to explore conversations between small groups (up to five people). In terms of user factors, we take into account conversation analysis, turn-taking and role-theory, for better understanding the impact of different user profiles. Our investigation allows us to report a detailed analysis on how delay influences the QoE, concluding that the actual interactivity pattern of each participant in the conversation results on different noticeability thresholds of delays. Such results have a direct impact on how we should design and construct video-communication services for multi-party conversations, where user activity should be considered as a prime adaptation and optimization parameter.

Invited Talk

The Multimedia Challenges in Social Media Analytics BIBAFull-Text 17-18
  Tat-Seng Chua
With the popularity and wide acceptance of social networks, users are now sharing information on multiple aspects of their life and on a wide range of social networks. In the meantime, there is a huge amount of situational information generated by sensor devices, often as part of human activities. Thus for any given entity, we can now find a wide variety of social, device and structured information from multiple sources. The generation of reliable social media analytics with respect to any entity is hence a highly challenging (multimedia) task. The key challenges include the ability to: (a) gather -- representative? data about an entity from multiple sources; (b) handle the increasing amount of non-textual media content; (c) detect and track sub-topics around the target entity, along with deep analysis tools such as named-entity extraction, visual concept detection and sentiment analysis; and (d) generate predictive and prescriptive analytics. This talk describes a live social observatory system that we have developed and our research efforts to tackle the above challenges. In particular, we outline our research to transform unstructured live social media streams into descriptive, predictive and prescriptive analytics.