Is Anyone Out There? Unpacking Q&A Hashtags on Twitter

In addition to posting news and status updates, many Twitter users post questions that seek various types of subjective and objective information. These questions are often labeled with "Q&A" hashtags, such as #lazyweb or #twoogle. We surveyed Twitter users and found they employ these Q&A hashtags both as a topical signifier (this tweet needs an answer!) and to reach out to those beyond their immediate followers (a community of helpful tweeters who monitor the hashtag). However, our log analysis of thousands of hashtagged Q&A exchanges reveals that nearly all replies to hashtagged questions come from a user's immediate follower network, contradicting user's beliefs that they are tapping into a larger community by tagging their question tweets. This finding has implications for designing next-generation social search systems that reach and engage a wide audience of answerers

Role of tipranavir in treatment of patients with multidrug-resistant HIV

The worldwide emergence of multidrug-resistant human immunodeficiency virus (HIV)-1 strains has the driven the development of new antiretroviral (ARV) agents. Over the past 5 years, HIV-entry and integrase inhibitor ARVs, as well as improved non-nucleoside reverse transcriptase inhibitors (NRTIs) and protease inhibitors (PIs), have become available for treatment. It is important to assess how these new ARVs might be most judiciously used, paying close attention to viral susceptibility patterns, pharmacodynamic parameters, and the likelihood that patients will adhere to their therapy. Herein we review published material in Medline, EMBASE, and ISI for each antiretroviral agent/classes currently approved and summarize the available data on their efficacy, safety, and pharmacologic parameters. We focus on the role of tipranavir, a recently approved nonpeptidic PI, for treating HIV-infected children, adolescents, and adults with a history of multidrug-resistant HIV

Does document relevance affect the searcher's perception 0f time?

Time plays an essential role in multiple areas of Information Retrieval (IR) studies such as search evaluation, user behavior analysis, temporal search result ranking and query understanding. Especially, in search evaluation studies, time is usually adopted as a measure to quantify users' efforts in search processes. Psychological studies have reported that the time perception of human beings can be affected by many stimuli, such as attention and motivation, which are closely related to many cognitive factors in search. Considering the fact that users' search experiences are affected by their subjective feelings of time, rather than the objective time measured by timing devices, it is necessary to look into the different factors that have impacts on search users' perception of time. In this work, we make a first step towards revealing the time perception mechanism of search users with the following contributions: (1) We establish an experimental research framework to measure the subjective perception of time while reading documents in search scenario, which originates from but is also different from traditional time perception measurements in psychological studies. (2) With the framework, we show that while users are reading result documents, document relevance has small yet visible effect on search users' perception of time. By further examining the impact of other factors, we demonstrate that the effect on relevant documents can also be influenced by individuals and tasks. (3) We conduct a preliminary experiment in which the difference between perceived time and dwell time is taken into consideration in a search evaluation task. We found that the revised framework achieved a better correlation with users' satisfaction feedbacks. This work may help us better understand the time perception mechanism of search users and provide insights in how to better incorporate time factor in search evaluation studies

We are the Change that we Seek: Information Interactions During a Change of Viewpoint

There has been considerable hype about filter bubbles and echo chambers influencing the views of information consumers. The fear is that these technologies are undermining democracy by swaying opinion and creating an uninformed, polarised populace. The literature in this space is mostly techno-centric, addressing the impact of technology. In contrast, our work is the first research in the information interaction field to examine changing viewpoints from a human-centric perspective. It provides a new understanding of view change and how we might support informed, autonomous view change behaviour. We interviewed 18 participants about a self-identified change of view, and the information touchpoints they engaged with along the way. In this paper we present the information types and sources that informed changes of viewpoint, and the ways in which our participants interacted with that information. We describe our findings in the context of the techno-centric literature and suggest principles for designing digital information environments that support user autonomy and reflection in viewpoint formation

What am I not seeing? An Interactive Approach to Social Content Discovery in Microblogs

In this paper, we focus on the informational and user experience benefits of user-driven topic exploration in microblog communities, such as Twitter, in an inspectable, controllable and personalized manner. To this end, we introduce ``HopTopics'' -- a novel interactive tool for exploring content that is popular just beyond a user's typical information horizon in a microblog, as defined by the network of individuals that they are connected to. We present results of a user study (N=122) to evaluate HopTopics with varying complexity against a typical microblog feed in both personalized and non-personalized conditions. Results show that the HopTopics system, leveraging content from both the direct and extended network of a user, succeeds in giving users a better sense of control and transparency. Moreover, participants had a poor mental model for the degree of novel content discovered when presented with non-personalized data in the Inspectable interface

An analysis of age, technology usage, and cognitive characteristics within information retrieval tasks

This work presents two studies that aim to discover whether age can be used as a suitable metric for distinguishing performance between individuals or if other factors can provide greater insight. Information retrieval tasks are used to test the performance of these factors. First, a study is introduced that examines the effect that fluid intelligence and Internet usage has on individuals. Second, a larger study is reported on that examines a collection of Internet and cognitive factors in order to determine to what extent each of these metrics can account for disorientation in users. This work adds to growing evidence showing that age is not a suitable metric to distinguish between individuals within the field of human-computer interaction. It shows that factors such as previous Internet experience and fluid-based cognitive abilities can be used to gain better insight into users' reported browsing experience during information retrieval tasks

The future of social is personal: the potential of the personal data store

This chapter argues that technical architectures that facilitate the longitudinal, decentralised and individual-centric personal collection and curation of data will be an important, but partial, response to the pressing problem of the autonomy of the data subject, and the asymmetry of power between the subject and large scale service providers/data consumers. Towards framing the scope and role of such Personal Data Stores (PDSes), the legalistic notion of personal data is examined, and it is argued that a more inclusive, intuitive notion expresses more accurately what individuals require in order to preserve their autonomy in a data-driven world of large aggregators. Six challenges towards realising the PDS vision are set out: the requirement to store data for long periods; the difficulties of managing data for individuals; the need to reconsider the regulatory basis for third-party access to data; the need to comply with international data handling standards; the need to integrate privacy-enhancing technologies; and the need to future-proof data gathering against the evolution of social norms. The open experimental PDS platform INDX is introduced and described, as a means of beginning to address at least some of these six challenges

Time-Sensitive User Profile for Optimizing Search Personlization

International audienceThanks to social Web services, Web search engines have the opportunity to afford personalized search results that better fit the user’s information needs and interests. To achieve this goal, many personalized search approaches explore user’s social Web interactions to extract his preferences and interests, and use them to model his profile. In our approach, the user profile is implicitly represented as a vector of weighted terms which correspond to the user’s interests extracted from his online social activities. As the user interests may change over time, we propose to weight profiles terms not only according to the content of these activities but also by considering the freshness. More precisely, the weights are adjusted with a temporal feature. In order to evaluate our approach, we model the user profile according to data collected from Twitter. Then, we rerank initial search results accurately to the user profile. Moreover, we proved the significance of adding a temporal feature by comparing our method with baselines models that does not consider the user profile dynamics

Probabilistic Reuse of Past Search Results

International audienceIn this paper, a new Monte Carlo algorithm to improve precision of information retrieval by using past search results is presented. Experiments were carried out to compare the proposed algorithm with traditional retrieval on a simulated dataset. In this dataset, documents, queries, and judgments of users were simulated. Exponential and Zipf distributions were used to build document collections. Uniform distribution was applied to build the queries. Zeta distribution was utilized to simulate the Bradford’s law representing the judgments of users. Empirical results show a better performance of our algorithm compared with traditional retrieval