Opportunities to address information poverty with social search

https://deepblue.lib.umich.edu/bitstream/2027.42/147350/1/Wheeler et al. Opportunities-address-information-pov CHI LBW2017.pd

Keyword Search in Social Networks

People often tend to ask their friends whenever they want some information related to topics like events, restaurants, or movies as majority of the search engines do not yield the desired results which people are seeking [1]. At present, majority of the current Open Source search engines like those based on Nutch also do not yield desired or expected results. Popular search engine, Google recently incorporated the feature of providing information from your social circle but only limited to Google Plus in your search results. On the other hand, micro blogging site Twitter has emerged as a vital source of information with more than 140 million active users [2] and nearly 250 million new tweets every day [2]. People also like to see more results from the blogs or news websites they follow and generally subscribe to their Really Simple Syndicate(RSS) [3] feed service to get the data and have to use RSS reader to find them. A web search engine which can provide results from user’s social network content along with the indexed web results would be a great deal of help for people interested in results from their social circle. This project’s goal is to include results from your Social Networks (Twitter, RSS feeds) in Yioop! search results by using feeds database created from your Twitter account and RSS feeds you follow

A looking-out portal (LOP) approach to enhance qualitative aspects of bandwidth utilisation in academic networks

Campuses of educational institutions periodically need to increase network bandwidth to keep up with increased demand and this decision is based on the quantitative aspects of the network bandwidth utilisation. The qualitative utilisation of the bandwidth is seldom looked into. Improving the qualitative utilisation of the bandwidth may not even necessitate a network upgrade. Although blacklist-based access control techniques help to a certain degree, the findings of this research indicate otherwise. A multi-tier, whitelist-based, looking-out portal (LOP) approach is presented that promises to improve the qualitative utilisation of the network while positively impacting pertinent resource identification and location of sources on the internet. The authors draw on their years of experience serving as students and staff in various campuses of universities and colleges in various countries while making recommendations

The value of social search: Seeking collective personal experience in social Q&A

In this paper, we examine the value of social question‐answering (Q&A) services as a platform for social search. We present a quasi‐field study where we instructed 20 study participants to use a social Q&A service, Yahoo! Answers, for a period of one week, and interviewed them about their experience with Yahoo! Answers based on the questions (N=99) they posted to the site. The results indicate that participants turned to a social search system when they needed firsthand information, diverse perspectives, and others' value judgments. Participants also preferred social search systems over web search engines in situations where they could obtain tailored information, access original and non‐popular information, filter out information, and interact with real people. Various strategies that participants employed to ensure that their questions would be likely to be answered were also identified. This study contributes to the field of information science by investigating a social Q&A service using the framework of social search from the information seeker's perspective. The results have implications for developers and designers of social search systems.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/106937/1/14505001067_ftp.pd

Social Search Behavior in a Social Q&A Service: Goals, Strategies, and Outcomes

information seekers to reach out to a larger, more distributed group of people online when searching for information. In this study, people’s question-asking behavior using a social Q&A service is conceptualized as social search behavior. We are particularly interested in investigating social search goals, strategies, tactics, informational outcomes, and social outcomes. We collected a total of 406 questions posted on Yahoo! Answers by 78 participants over one week. Interviews based on those questions and answers they received were conducted and content-analyzed. We identify five distinct search strategies and 15 tactics positioned on a continuum of two different dimensions in terms of answer quantity and answer quality. Pursuit of quantity or quality is influenced by five categories of goals identified in this study. The goals and associated strategies and tactics also influence people’s perceived informational outcomes and social outcomes. Contributions of this study to the social search research community and implications for practitioners in the area of social Q&A services are discussed.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/115871/1/Jeon Rieh ASIST 2015 published.pd

Applying Short-term Memory to Social Search Agents

AbstractThis paper presents about our research in social search. Generally, the research in social search falls into two principal challenges. The first challenge is how to find more relevant answers to the question. The second one is how to increase speed in finding relevant answers.Recently, we had provided two algorithms called Asknext and Question Waves to find more relevant answers compared to the baseline algorithm BFS. But, the search speed of the two proposed algorithms still the subject to improve.In this paper, we introduce the agents’ ability of learning the answers from the interactions with other agents so that they can quickly answer the question of other agents. We model this learning process by implementing the concept of data caching as the short-term memory of each social search agent. The result improvement of the speediness and the reduction of the number of messages used to communicate between agents, after apply agent's short-term memory concept, demonstrates the usefulness of the proposed approach

Beyond Search: A Technology Probe Investigation

Purpose — We assert that researchers developing new web interaction tools should consider an array of user motives beyond query-based information retrieval. This chapter reports on two probes used to investigate user activities that go beyond search as traditionally conceived. Design/methodology — This chapter reviews research on user experiences with search engines and general web use. It then describes the design and case study of cards and pebbles, two search engine-based probes developed to help elicit new concepts for web-based experiences. Findings — Participants reflect on their experiences with the probes and offer ideas regarding how to incrementally shift the traditional search paradigm and conceive of the web in new ways. Implications/value — This investigation serves as a starting point by offering criteria that should be considered when designing new ‘beyond search’ tools

Trust-Based Techniques for Collective Intelligence in Social Search Systems.

A key-issue for the effectiveness of collaborative decision support systems is the problem of the trustworthiness of the entities involved in the process. Trust has been always used by humans as a form of collective intelligence to support effective decision making process. Computational trust models are becoming now a popular technique across many applications such as cloud computing, p2p networks, wikis, e-commerce sites, social network. The chapter provides an overview of the current landscape of computational models of trust and reputation, and it presents an experimental study case in the domain of social search, where we show how trust techniques can be applied to enhance the quality of social search engine predictions

Understanding Crowd-Powered Search Groups: A Social Network Perspective

Background: Crowd-powered search is a new form of search and problem solving scheme that involves collaboration among a potentially large number of voluntary Web users. Human flesh search (HFS), a particular form of crowd-powered search originated in China, has seen tremendous growth since its inception in 2001. HFS presents a valuable test-bed for scientists to validate existing and new theories in social computing, sociology, behavioral sciences, and so forth. Methodology: In this research, we construct an aggregated HFS group, consisting of the participants and their relationships in a comprehensive set of identified HFS episodes. We study the topological properties and the evolution of the aggregated network and different sub-groups in the network. We also identify the key HFS participants according to a variety of measures. Conclusions: We found that, as compared with other online social networks, HFS participant network shares the power-law degree distribution and small-world property, but with a looser and more distributed organizational structure, leading to the diversity, decentralization, and independence of HFS participants. In addition, the HFS group has been becoming increasingly decentralized. The comparisons of different HFS sub-groups reveal that HFS participants collaborated more often when they conducted the searches in local platforms or the searches requiring a certain level of professional knowledge background. On the contrary, HFS participants did not collaborate much when they performed the search tas

Social search : application, possibilities and challenges

Razvoj društvenih medija i weba 2.0. omogućio je iskorištavanje društveno stvorenog sadržaja kako bi se poboljšali rezultati pretraživanja. U radu se prikazuju mogućnosti primjene društvenog pretraživanja, njegove prednosti i nedostaci. Cilj je pokušati odgovoriti na pitanje da li se društveno pretraživanje može uspješno koristiti kao nadopuna tradicionalnom pretraživanju. Tradicionalno pretraživanje često ostavlja korisnike nezadovoljnima jer nisu uspjeli pronaći odgovor na svoju informacijsku potrebu. Nekada se to događa zbog samih korisnika koji ne znaju pravilno oblikovati svoju potrebu u upit, a nekada zbog toga što pretraživači zanemaruju kontekst pretraživanja i specifične potrebe pojedinog korisnika. Društveno pretraživanje pokušava razriješiti oba navedena problema. U prvom je dijelu rada opisan pojam pretraživanja informacija i objašnjen način na kojem funkcionira tradicionalno pretraživanje. Navedene su vrste modela pretraživanja, korisničkih upita i podjela korisnika prema stručnosti pretraživanja, te su analizirani utjecaji koje je razvoj World Wide Weba imao na pretraživanje informacija. U drugom dijelu rada prikazane su različite primjene društvenog pretraživanja, a kroz primjere objašnjen je način njihovog funkcioniranja i arhitektura sustava.Social Media and development of Web 2.0. made it possible to make use of sociallygenerated content to improve search results. The paper presents the possibilities of social search, its advantages and disadvantages. The goal is to try to answer whether social search can be successfully used to complement traditional search. Traditional search often leaves users dissatisfied because they have been unable to find an answer to their information need. Sometimes this happens because the users do not know how to properly convey their need into query, and sometimes because search engines ignore the search context and specific needs of the individual user. Social search is trying to solve both of these problems. The first part of the paper describes the concept of information retrieval and explains how traditional search works. Types of search models, user queries, and user taxonomy are listed, and the impacts that the development of the World Wide Web has had on information retrieval have been analyzed. In the second part of the paper, various applications of social search are presented, and through the examples system architecture and the way they function is explained