An elementary social information foraging model

Lecture Notes in Computer Science, 2009

The World Wide Web is growing in size and with the proliferation of large-scale collaborative computing environments Social search has become increasingly important. The focal point of this recent field is to assign relevance and trustworthiness to web-pages by taking into account the reader's perspective rather than web-masters' point of view. Current web-searching technologies tend to rely on explicit human recommendations, in part because it is hard to obtain user' feedback however these methods are hard to scale. Implicit feedback techniques are a potentially useful alternative. The challenge is in producing implicit web-rankings by reasoning over users' activity during a web-search but without recourse to explicit human intervention. This paper focuses on a novel Social Search formal model based on Information Foraging Theory, showing a different way to implicitly judge web entities by considering effort expended by users in viewing them. 100 university students were recruited to explicitly evaluate the usefulness of 12 thematic web-sites and an experiment was performed implicitly gathering their web-browsing activity. Correlation indexes were adopted and encouraging results where obtained suggesting the existence of a considerable relationship between explicit feedback and implicit derived judgements. Furthermore, a comparison of the results obtained and the results provided by Google was performed. The proposed nature-inspired approach shows that, by considering the same searching query, Social search to be more effective than the Google Page-Rank Algorithm. This evidence supports the presentation of a novel general schema for a Social search engine generating implicit web-rankings by taking into account the Collective Intelligence emerged from users by reasoning on their behaviour.

Information Processing & Management, 2009

Communication is considered to be one of the most essential components of collaboration, but our understanding as to which form of communication provides the most optimal costbenefit balance lacks severely. To help investigate effects of various communication channels on a collaborative project, we conducted a user study with 30 pairs (60 participants) in three different conditions -co-located, remotely located with text chat, and remotely located with text as well as audio chat, in an exploratory search task. Using both quantitative and qualitative data analysis, we found that teams with remotely located participants were more effective in terms of being able to explore more diverse information. Adding audio support for remote collaboration helped participants to lower their cognitive load as well as negative emotions compared to those working in the same space. We also show how these findings could help design more effective systems for collaborative information seeking tasks using adequate and appropriate communication. We argue that collaboration is an important aspect of human-centered IR, and that our work provides interesting insights into people doing information seeking/retrieval in collaboration.

2008

2011

ABSTRACT When searching for information, people often seek help from others. However, while people can benefit from communicating with others, they can usually satisfy their information needs, to some degree, without help. Because establishing explicit collaborations is often seen as onerous, there are many missed opportunities where shared experiences could save time and effort.

Information Retrieval Journal, 2019

While today's web search engines are designed for single-user search, over the years research efforts have shown that complex information needswhich are explorative, open-ended and multi-faceted-can be answered more efficiently and effectively when searching in collaboration. Collaborative search (and sensemaking) research has investigated techniques, algorithms and interface affordances to gain insights and improve the collaborative search process. It is not hard to imagine that the size of the group collaborating on a search task significantly influences the group's behaviour and search effectiveness. However, a common denominator across almost all existing studies is a fixed group size-usually either pairs, triads or in a few cases four users collaborating. Investigations into larger group sizes and the impact of group size dynamics on users' behaviour and search metrics have so far rarely been considered-and when, then only in a simulation setup. In this work, we investigate in a large-scale user experiment to what extent those simulation results carry over to the real world. To this end, we extended our collaborative search framework SearchX with algorithmic mediation features and ran a large-scale experiment with more than 300 crowd-workers. We consider the collaboration group size as a dependent variable, and investigate collaborations between groups of up to six people. We find that most prior simulation-based results on the impact of collaboration group size on behaviour and search effectiveness cannot be reproduced in our user experiment. Collaborative search and more generally online collaborative information seeking have been shown to be effective tools to tackle complex information needs,

Computer, 2000

Proceedings of the American Society for Information Science and Technology, 2008

Collaboration plays an important role in the information seeking and retrieval activities within a team setting. In this research, we examined the impact of collaborative design features in two information retrieval tools that explicitly support collaboration. We designed the two collaborative information searching prototype, MUSE (Multi-User Search Engine) and MUST (Multi-User Search and Talk) and evaluated both systems. Results indicate that the communication (i.e., chat) function played an important role in enhancing the information seeking process by establishing common ground among group members. We also identified unexpected challenges that arose as the prototypes were used during these activities. These challenges were both technical and social in nature. We discuss implications for system design and directions for future research.

2011

This chapter considers the social component of interactive information retrieval: what is the role of other people in searching and browsing? For simplicity we begin by considering situations without computers. After all, you can interactively retrieve information without a computer; you just have to interact with someone or something else.

Proceedings of the American Society for Information Science and Technology, 2012

There has been a growing interest within the information sciences and HCI communities to understand the role of collaboration in facilitating information seeking. This focus had led to the emergence of the research area of collaborative information seeking (CIS). Although researchers are starting to identify various activities and mechanisms that underlie CIS, we know very little about the barriers to CIS. In this study, we used Mechanical Turk (MTurk) to gather data from 307 participants to understand the barriers to CIS in organizations. Through our data analysis, we identified a variety of barriers that hinder CIS. These barriers fell under four broad categoriesorganizational, technical, individual, and team. These barriers also had a strong temporal component which we highlight in the discussion. From these findings, we discuss some design implications for information retrieval systems.