In 1974 an article appeared in Science magazine with the dry-sounding title “Judgment Under Uncertainty: Heuristics and Biases” by a pair of psychologists who were not well known outside their discipline of decision theory. In it Amos Tversky and Daniel Kahneman introduced the world to Prospect Theory, which mapped out how humans actually behave when faced with decisions about gains and losses, in contrast to how economists assumed that people behave. Prospect Theory turned Economics on its head by demonstrating through a series of ingenious experiments that people are much more concerned with losses than they are with gains, and that framing a choice from one perspective or the other will result in decisions that are exactly the opposite of each other, even if the outcomes are monetarily the same. Prospect Theory led cognitive psychology in a new direction that began to uncover other human biases in thinking that are probably not learned but are part of our brain’s wiring.

Thinking fast and slow.

Garfinkel and Ethnomethodology

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Elements Of Episodic Memory

With the popularity of smartphones and mobile devices, mobile application (a.k.a. “app”) markets have been growing exponentially in terms of number of users and downloads. App developers spend considerable effort on collecting and exploiting user feedback to improve user satisfaction, but suffer from the absence of effective user review analytics tools. To facilitate mobile app developers discover the most “informative” user reviews from a large and rapidly increasing pool of user reviews, we present “AR-Miner” — a novel computational framework for App Review Mining, which performs comprehensive analytics from raw user reviews by (i) first extracting informative user reviews by filtering noisy and irrelevant ones, (ii) then grouping the informative reviews automatically using topic modeling, (iii) further prioritizing the informative reviews by an effective review ranking scheme, (iv) and finally presenting the groups of most “informative” reviews via an intuitive visualization approach. We conduct extensive experiments and case studies on four popular Android apps to evaluate AR-Miner, from which the encouraging results indicate that AR-Miner is effective, efficient and promising for app developers.

https://ink.library.smu.edu.sg/cgi/viewcontent.cgi?article=3323&context=sis_research

AR-miner: mining informative reviews for developers from mobile app marketplace

Twitter is now used to distribute substantive content such as breaking news, increasing the importance of assessing the credibility of tweets. As users increasingly access tweets through search, they have less information on which to base credibility judgments as compared to consuming content from direct social network connections. We present survey results regarding users' perceptions of tweet credibility. We find a disparity between features users consider relevant to credibility assessment and those currently revealed by search engines. We then conducted two experiments in which we systematically manipulated several features of tweets to assess their impact on credibility ratings. We show that users are poor judges of truthfulness based on content alone, and instead are influenced by heuristics such as user name when making credibility assessments. Based on these findings, we discuss strategies tweet authors can use to enhance their credibility with readers (and strategies astute readers should be aware of!). We propose design improvements for displaying social search results so as to better convey credibility.

/pdf/tweeting-is-believing-understanding-microblog-credibility-4jts8ayo5y.pdf

Tweeting is believing?: understanding microblog credibility perceptions

Third-party applications (apps) drive the attractiveness of web and mobile application platforms. Many of these platforms adopt a decentralized control strategy, relying on explicit user consent for granting permissions that the apps request. Users have to rely primarily on community ratings as the signals to identify the potentially harmful and inappropriate apps even though community ratings typically reflect opinions about perceived functionality or performance rather than about risks. With the arrival of HTML5 web apps, such user-consent permission systems will become more widespread. We study the effectiveness of user-consent permission systems through a large scale data collection of Facebook apps, Chrome extensions and Android apps. Our analysis confirms that the current forms of community ratings used in app markets today are not reliable indicators of privacy risks of an app. We find some evidence indicating attempts to mislead or entice users into granting permissions: free applications and applications with mature content request more permissions than is typical; 'look-alike' applications which have names similar to popular applications also request more permissions than is typical. We also find that across all three platforms popular applications request more permissions than average.

Is this app safe?: a large scale study on application permissions and risk signals

In this paper, we propose a system for helping users to judge the credibility of Web search results and to search for credible Web pages. Conventional Web search engines present only titles, snippets, and URLs for users, which give few clues to judge the credibility of Web search results. Moreover, ranking algorithms of the conventional Web search engines are often based on relevance and popularity of Web pages. Towards credibility-oriented Web search, our proposed system provides users with the following three functions: (1) calculation and visualization of several scores of Web search results on the main credibility aspects, (2) prediction of user's credibility judgment model through user's credibility feedback for Web search results, and (3) re-ranking of Web search results based on user's predicted credibility model. Experimental results suggest that our system enables users - in particular, users with knowledge about search topics - to find credible Web pages from a list of Web search results more efficiently than conventional Web search interfaces.

Enhancing credibility judgment of web search results

If the user wants to know trustworthiness of a proposition, such as whether gthe Japanese Prime Minister is Junichiro Koizumih is true or false, conventional search engines are not appropriate. We therefore propose a system that helps the user to determine trustworthiness of a statement that he or she is unconfident about. In our research, we estimate trustworthiness of a proposition by aggregating knowledge from the Web and analyzing creation time of web pages. We propose a method to estimate popularity from temporal viewpoint by analyzing how many pages discussed the proposition in a certain period of time and how continuously it appeared on the Web.

/pdf/honto-search-estimating-trustworthiness-of-web-information-283o0lqpyd.pdf

Honto? search: estimating trustworthiness of web information by search results aggregation and temporal analysis

We have developed a system for helping users to determine the trustworthiness of uncertain facts based on sentiment and temporal viewpoints by aggregating information from the Web. Our goal is not to determine whether uncertain facts are true or false, but to provide users with additional data on which the trustworthiness of the information can be determined. The system shows with what sentiment and in what context facts are mentioned on the Web and displays any temporal change in the fact's popularity. Furthermore, the system extracts counter facts and analyzes them in the same way. The majority of 1000 users who evaluated our system found it to be a useful tool for helping to determine the trustworthiness of facts from various viewpoints.

Supporting Judgment of Fact Trustworthiness Considering Temporal and Sentimental Aspects

We propose query priming to activate careful user information seeking in web searches. Query priming employs query auto-completion (QAC) and query suggestion (QS) to present search terms that stimulate critical thinking and encourages careful information seeking and decision making. We conducted an online user study using a crowdsourcing service. Analysis of search behavior logs and questionnaire responses confirmed the following. (1) With query priming, participants issued more queries and (re-)visited search engine result pages more frequently. (2) Query priming promoted webpage selection targeted at evidence-based decision making. (3) The query priming effect varied relative to participant educational background. This study contributes to search interaction design to enhance user engagement in critical thinking in web searches.

Yusuke Yamamoto

Papers

Is this app safe?: a large scale study on application permissions and risk signals

Enhancing credibility judgment of web search results

Honto? search: estimating trustworthiness of web information by search results aggregation and temporal analysis

Supporting Judgment of Fact Trustworthiness Considering Temporal and Sentimental Aspects

Query Priming for Promoting Critical Thinking in Web Search