Gangs, displaced, and group-based aggression

Many urban areas experienced an alarming growth of gang activity and violence during the end of the 20th and the beginning of the 21st centuries. Gang members, motivated by various factors, commit a variety of different types of violent acts towards rivals and other targets. Our focus involves instances of displaced aggression, which generally refers to situations in which aggression is targeted towards individuals who have either not themselves committed an offense against the aggressor (s), or who provide an offense that is too mild to justify the aggression levels that are expressed towards them. We discuss how social–psychological mechanisms and models of two types of displaced aggression might help explain some aspects of the retaliatory behavior that is expressed by members of street gangs. We also propose general techniques that have the potential to reduce such aggressive behavior

Internet and Users. Who is the Reader?

Internet has turned into a fundamental component of everyday life, as it plays a major role in advancing the globalization process. Globalization was fostered by the idea of creating equalaccess opportunities for all and facilitating communication worldwide. Using internet as the core platform, billions of people try to access and benefit from this opportunity through search engines, service providers, websites and social media. However, given the profound difference between internet and user’s languages, users end up on relying on search engines and tools to translate their ideas into a computer-readable language and derive information from them. In order to provide the best possible services, search engines and social media need to accumulate comprehensive data on each user’s identity. The challenge is that once they are fed with convenient information on each user, they tend to personalize the idea they grasp of him or her based on their given regulations and policies, which in the mid- and long-term results in managing users’ access to information.. By applying the reader-response theory, this paper seeks to focus on the challenges stemming from the adoption of users’ personalized profiles by Google, Facebook and Amazon as the most common part of users’ performance in internet. It also explores how the reading differences of the users and the tools result not only in personalized versions of users, but also engender an unrecognized virtual in-betweenness of users’ own perception of themselves and the tools’ perception of users

The Shortest Path to Happiness: Recommending Beautiful, Quiet, and Happy Routes in the City

When providing directions to a place, web and mobile mapping services are all able to suggest the shortest route. The goal of this work is to automatically suggest routes that are not only short but also emotionally pleasant. To quantify the extent to which urban locations are pleasant, we use data from a crowd-sourcing platform that shows two street scenes in London (out of hundreds), and a user votes on which one looks more beautiful, quiet, and happy. We consider votes from more than 3.3K individuals and translate them into quantitative measures of location perceptions. We arrange those locations into a graph upon which we learn pleasant routes. Based on a quantitative validation, we find that, compared to the shortest routes, the recommended ones add just a few extra walking minutes and are indeed perceived to be more beautiful, quiet, and happy. To test the generality of our approach, we consider Flickr metadata of more than 3.7M pictures in London and 1.3M in Boston, compute proxies for the crowdsourced beauty dimension (the one for which we have collected the most votes), and evaluate those proxies with 30 participants in London and 54 in Boston. These participants have not only rated our recommendations but have also carefully motivated their choices, providing insights for future work.Comment: 11 pages, 7 figures, Proceedings of ACM Hypertext 201

Personalization via collaboration in web retrieval systems: a context based approach

World Wide Web is a source of information, and searches on the Web can be analyzed to detect patterns in Web users' search behaviors and information needs to effectively handle the users' subsequent needs. The rationale is that the information need of a user at a particular time point occurs in a particular context, and queries are derived from that need. In this paper, we discuss an extension of our personalization approach that was originally developed for a traditional bibliographic retrieval system but has been adapted and extended with a collaborative model for the Web retrieval environment. We start with a brief introduction of our personalization approach in a traditional information retrieval system. Then, based on the differences in the nature of documents, users and search tasks between traditional and Web retrieval environments, we describe our extensions of integrating collaboration in personalization in the Web retrieval environment. The architecture for the extension integrates machine learning techniques for the purpose of better modeling users' search tasks. Finally, a user-oriented evaluation of Web-based adaptive retrieval systems is presented as an important aspect of the overall strategy for personalization

Media in Crisis: Journalistic Norms in Natural Disaster Coverage

Nearing the end of 2017, the United States and the Caribbean were struck with back-to-back natural disasters that left the country in shock and turmoil. Among the three hurricanes that struck sequentially, Hurricane Harvey landed in Texas approximately on August 25th, 2017 and Hurricane Maria hit the Caribbean and Puerto Rico around September 20th, 2017. These disasters were a test for the new presidential cabinet of how they would handle their first natural disaster. Hurricane Maria and Hurricane Harvey caused similar levels of destruction, with Maria being a category five storm and Harvey a category four. However, the media reacted differently to the crisis that unfolded in Puerto Rico in comparison to Texas. This study focuses on how journalists and the news industry covered both disasters and compares and contrasts the manner in which they were done. Pulling in media industry knowledge, rhetoric and cultural theory, the study uncovers how disaster communication was influenced by societal values involving culture and examines how the narrative journalists participated in affected the coverage, in turn shaping public knowledge

Automated user modeling for personalized digital libraries

Digital libraries (DL) have become one of the most typical ways of accessing any kind of digitalized information. Due to this key role, users welcome any improvements on the services they receive from digital libraries. One trend used to improve digital services is through personalization. Up to now, the most common approach for personalization in digital libraries has been user-driven. Nevertheless, the design of efficient personalized services has to be done, at least in part, in an automatic way. In this context, machine learning techniques automate the process of constructing user models. This paper proposes a new approach to construct digital libraries that satisfy user’s necessity for information: Adaptive Digital Libraries, libraries that automatically learn user preferences and goals and personalize their interaction using this information

NoTube – making TV a medium for personalized interaction

In this paper, we introduce NoTube’s vision on deploying semantics in interactive TV context in order to contextualize distributed applications and lift them to a new level of service that provides context-dependent and personalized selection of TV content. Additionally, lifting content consumption from a single-user activity to a community-based experience in a connected multi-device environment is central to the project. Main research questions relate to (1) data integration and enrichment - how to achieve unified and simple access to dynamic, growing and distributed multimedia content of diverse formats? (2) user and context modeling - what is an appropriate framework for context modeling, incorporating task-, domain and device-specific viewpoints? (3) context-aware discovery of resources - how could rather fuzzy matchmaking between potentially infinite contexts and available media resources be achieved? (4) collaborative architecture for TV content personalization - how can the combined information about data, context and user be put at disposal of both content providers and end-users in the view of creating extremely personalized services under controlled privacy and security policies? Thus, with the grand challenge in mind - to put the TV viewer back in the driver's seat – we focus on TV content as a medium for personalized interaction between people based on a service architecture that caters for a variety of content metadata, delivery channels and rendering devices

A New Account of Personalization and Effective Communication

To contribute to understanding of information economies of daily life, this paper explores over the past millennium given names of a large number of persons. Analysts have long both condemned and praised mass media as a source of common culture, national unity, or shared symbolic experiences. Names, however, indicate a large decline in shared symbolic experience over the past two centuries, a decline that the growth of mass media does not appear to have affected significantly. Study of names also shows that action and personal relationships, along with time horizon, are central aspects of effective communication across a large population. The observed preference for personalization over the past two centuries and the importance of action and personal relationships to effective communication are aspects of information economies that are likely to have continuing significance for industry developments, economic statistics, and public policy

A Personalized System for Conversational Recommendations

Searching for and making decisions about information is becoming increasingly difficult as the amount of information and number of choices increases. Recommendation systems help users find items of interest of a particular type, such as movies or restaurants, but are still somewhat awkward to use. Our solution is to take advantage of the complementary strengths of personalized recommendation systems and dialogue systems, creating personalized aides. We present a system -- the Adaptive Place Advisor -- that treats item selection as an interactive, conversational process, with the program inquiring about item attributes and the user responding. Individual, long-term user preferences are unobtrusively obtained in the course of normal recommendation dialogues and used to direct future conversations with the same user. We present a novel user model that influences both item search and the questions asked during a conversation. We demonstrate the effectiveness of our system in significantly reducing the time and number of interactions required to find a satisfactory item, as compared to a control group of users interacting with a non-adaptive version of the system