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CrowdSens Tables of Contents: 12

Proceedings of the 2012 International Workshop on Multimodal Crowd Sensing

Fullname:Proceedings of the 1st International Workshop on Multimodal Crowd Sensing
Editors:Haggai Roitman; Iván Cantador; Miriam Fernandez
Location:Maui, Hawaii
Standard No:ISBN: 978-1-4503-1715-3; ACM DL: Table of Contents; hcibib: CrowdSens12
Links:Workshop Website | Conference Website
  1. Keynote address
  2. Full papers
  3. Invited talk
  4. Short papers

Keynote address

Crowdsourcing in the enterprise BIBAFull-Text 1-2
  Ido Guy
This talk reviews several of the recent studies conducted by the Social Technologies group at IBM Research-Haifa, which demonstrate the use of social analytics tools to extract value of enterprise social media. From recommender systems, through activity stream filtering and analysis, to crowdsourcing games in the enterprise, the voice of the employees can now be heard and utilized better than ever within the newly formed social business.

Full papers

Conceptual modeling principles for crowdsourcing BIBAFull-Text 3-6
  Roman Lukyanenko; Jeffrey Parsons
Traditionally, the research and practice of conceptual modeling assumed all relevant information about a domain could be discovered through user-analyst communication. The increasing ubiquity of crowdsourcing challenges a number of long-held propositions about conceptual modeling. Given significant differences in levels of domain expertise among contributors in crowdsourcing projects, it is often impossible to predict all valid conceptualizations of a domain by potential users. Approaching conceptual modeling in crowdsourcing using traditional principles of modeling is highly constraining. This paper explores fundamental conceptual modeling challenges in crowdsourcing domains. We then use theoretical foundations in philosophy (ontology) to offer potential solutions.
Event detection using Twitter and structured semantic query expansion BIBAFull-Text 7-14
  Heather S. Packer; Sina Samangooei; Jonathon S. Hare; Nicholas Gibbins; Paul H. Lewis
Twitter is a popular tool for publishing potentially interesting information about people's opinions, experiences and news. Mobile devices allow people to publish tweets during real-time events. It is often difficult to identify the subject of a tweet because Twitter users often write using highly unstructured language with many typographical errors. Structured data related to entities can provide additional context to tweets. We propose an approach which associates tweets to a given event using query expansion and relationships defined on the Semantic Web, thus increasing the recall whilst maintaining or improving the precision of event detection. In this work, we investigate the usage of Twitter in discussing the Rock am Ring music festival. We aim to use prior knowledge of the festival's lineup to associate tweets with the bands playing at the festival. In order to evaluate the effectiveness of our approach, we compare the lifetime of the Twitter buzz surrounding an event to the actual programmed event, using Twitter users as social sensors.

Invited talk

Using friends as sensors to detect planetary-scale contagious outbreaks BIBAFull-Text 15-16
  Manuel Cebrian
Recent research has focused on the monitoring of planetary-scale online data for improved detection of flu outbreak [1], global mood patterns [2], movements in the stock market [3], political revolutions [4], and many other important phenomena. However, none of these methods take advantage of the local properties of the network structure in these data sources to identify key nodes. This is important because, on the one hand, increasingly the amount of data available online exceeds our capacity to monitor it in real time, and on the other hand, privacy considerations will place restrictions on the accessibility of partial/global regions of our social network [?] Here we develop an analytic model of the contagious spread of information in a large-scale publicly-articulated social network and show that Feld's paradox [5], stated as "your friends have more friends than you do", tested previously previous a previous study of a flu outbreak at Harvard College [6] can yield not just early detection, but advance warning of contagious outbreaks. In this method, we randomly choose a small fraction of nodes in the network and then we randomly choose a "friend" of each node to include in a group for monitoring. Using 6 months of data from Twitter, we show that this friend group is more central in the network and it helps us to detect viral outbreaks of the use of novel hashtags about 6 days earlier than we could with an equal-sized randomly chosen group. Moreover, the method actually works better than predicted by theory, suggesting that traits correlated with being central increase the likelihood that outbreaks will originate in the friend group. These traits include increased activity and increased diversity in the information transmitted by this group relative to other groups.

Short papers

Harnessing the crowds for smart city sensing BIBAFull-Text 17-18
  Haggai Roitman; Jonathan Mamou; Sameep Mehta; Aharon Satt; L. V. Subramaniam
In this work we discuss the challenge of harnessing the crowd for smart city sensing. Within a city's context, such reports by citizen or city visitor eye witnesses may provide important information to city officials, additionally to more traditional data gathered by other means (e.g., through the city's control center, emergency services, sensors spread across the city, etc). We present an high-level overview of a novel crowd sensing system that we develop in IBM for the smart cities domain. As a proof of concept, we present some preliminary results using public safety as our example usecase.
"Greaaaat bargains starting from just 99p!!!! :-)": brand perception in the social media BIBAFull-Text 19-20
  Michal Shmueli-Scheuer; Benjamin Sznajder; Doron Cohen; Ariel Raviv; David Konopnicki; Haggai Roitman
In this work we discuss the challenges of utilizing social media data, and more specifically microblogs, for helping brand managers. Brand perception is one of the most important tasks of a brand manager, requiring to understand how customers perceive and select brands in specific product categories or market segments. While understanding the brand perception from conventional sources such as reviews and advertisement is well studied and established, gaining insights from social media sources is still an open challenge. In this paper, we present a high-level overview of a novel system that was developed in IBM which aims at extracting brand perception from Twitter. As a proof of concept, we present some preliminary results from the retail domain.