Inferring what a user is not interested in

Almraet Th/.¢ paper dnscv/bes a sys'tfm we haw de~/optJd to bnpmw ~ sp~ed wtd sacce~ rote w/th wMch wws bmw~ aoJh,~//brav/~ The s3etem/, a/eambtg Learning Apprentice for Browsing "Browsing" is the searching of a computer hl~aty for an individmd ~ item. The bnnmn doing the search (the "user') ,i,,, to find an item (the "target') that best meets his/her nxluimn~ts. The user's mental model of the tazget is called the "sentr, h goal'. Our testbed browsing applic~ion is software rense. The h'bmx7 is a collection of object-oriented softwa~. An item in the library is a "class" containing locally defined "instance vaziables" and "methods'. A class also inherits the va6ables and methods of its superclass in the inhesitance hletat~y. A class's functionality is detetm/ned by its methods. The aim of browsing is to find the class whose functionality is closest to the requinKi functional/ty. In our browsing system the user is initially presented with a list of all the claues in the h'bnwy. As browsing proceeds additional class lists and method lists are created by the nsef's actions. To apply an operator to a class, the user selects the class from any available class list and then specifies the operator to be applied. An example of a clau-hased operator is "Defined Methods"; when applied to class C this creates a list of the methods C defines locally. To apply an operator to a method is a two step process. Hnt one must select the method in the method fist produced by "Defmed Methods'. ~ "opens" the method in a window that is used for inspecting a method's details. To apply an operate, the user must "magk" one of more methods in this window and then specLCy the operator-For example the operator "Used By" creates a list of classes oniesed by the degnm to which each uses ill the cun, ently marked methods. A cless's score is based on the si~ of the madmd methods' names to the nmnes of the methods that are called by the class's own methods

User-centric Privacy Engineering for the Internet of Things

User privacy concerns are widely regarded as a key obstacle to the success of modern smart cyber-physical systems. In this paper, we analyse, through an example, some of the requirements that future data collection architectures of these systems should implement to provide effective privacy protection for users. Then, we give an example of how these requirements can be implemented in a smart home scenario. Our example architecture allows the user to balance the privacy risks with the potential benefits and take a practical decision determining the extent of the sharing. Based on this example architecture, we identify a number of challenges that must be addressed by future data processing systems in order to achieve effective privacy management for smart cyber-physical systems.Comment: 12 Page

Search Bias Quantification: Investigating Political Bias in Social Media and Web Search

Users frequently use search systems on the Web as well as online social media to learn about ongoing events and public opinion on personalities. Prior studies have shown that the top-ranked results returned by these search engines can shape user opinion about the topic (e.g., event or person) being searched. In case of polarizing topics like politics, where multiple competing perspectives exist, the political bias in the top search results can play a significant role in shaping public opinion towards (or away from) certain perspectives. Given the considerable impact that search bias can have on the user, we propose a generalizable search bias quantification framework that not only measures the political bias in ranked list output by the search system but also decouples the bias introduced by the different sources—input data and ranking system. We apply our framework to study the political bias in searches related to 2016 US Presidential primaries in Twitter social media search and find that both input data and ranking system matter in determining the final search output bias seen by the users. And finally, we use the framework to compare the relative bias for two popular search systems—Twitter social media search and Google web search—for queries related to politicians and political events. We end by discussing some potential solutions to signal the bias in the search results to make the users more aware of them.publishe

Characterizing Information Diets of Social Media Users

With the widespread adoption of social media sites like Twitter and Facebook, there has been a shift in the way information is produced and consumed. Earlier, the only producers of information were traditional news organizations, which broadcast the same carefully-edited information to all consumers over mass media channels. Whereas, now, in online social media, any user can be a producer of information, and every user selects which other users she connects to, thereby choosing the information she consumes. Moreover, the personalized recommendations that most social media sites provide also contribute towards the information consumed by individual users. In this work, we define a concept of information diet -- which is the topical distribution of a given set of information items (e.g., tweets) -- to characterize the information produced and consumed by various types of users in the popular Twitter social media. At a high level, we find that (i) popular users mostly produce very specialized diets focusing on only a few topics; in fact, news organizations (e.g., NYTimes) produce much more focused diets on social media as compared to their mass media diets, (ii) most users' consumption diets are primarily focused towards one or two topics of their interest, and (iii) the personalized recommendations provided by Twitter help to mitigate some of the topical imbalances in the users' consumption diets, by adding information on diverse topics apart from the users' primary topics of interest.Comment: In Proceeding of International AAAI Conference on Web and Social Media (ICWSM), Oxford, UK, May 201

Equality of Voice: Towards Fair Representation in Crowdsourced Top-K Recommendations

To help their users to discover important items at a particular time, major websites like Twitter, Yelp, TripAdvisor or NYTimes provide Top-K recommendations (e.g., 10 Trending Topics, Top 5 Hotels in Paris or 10 Most Viewed News Stories), which rely on crowdsourced popularity signals to select the items. However, different sections of a crowd may have different preferences, and there is a large silent majority who do not explicitly express their opinion. Also, the crowd often consists of actors like bots, spammers, or people running orchestrated campaigns. Recommendation algorithms today largely do not consider such nuances, hence are vulnerable to strategic manipulation by small but hyper-active user groups. To fairly aggregate the preferences of all users while recommending top-K items, we borrow ideas from prior research on social choice theory, and identify a voting mechanism called Single Transferable Vote (STV) as having many of the fairness properties we desire in top-K item (s)elections. We develop an innovative mechanism to attribute preferences of silent majority which also make STV completely operational. We show the generalizability of our approach by implementing it on two different real-world datasets. Through extensive experimentation and comparison with state-of-the-art techniques, we show that our proposed approach provides maximum user satisfaction, and cuts down drastically on items disliked by most but hyper-actively promoted by a few users.Comment: In the proceedings of the Conference on Fairness, Accountability, and Transparency (FAT* '19). Please cite the conference versio