Archive for the ‘Association for Computing Machinery’ Category

Hooked on Smartphones: An Exploratory Study on Smartphone Overuse among College Students

April 16, 2014 Comments off

Hooked on Smartphones: An Exploratory Study on Smartphone Overuse among College Students (PDF)
Source: Association for Computing Machinery

The negative aspects of smartphone overuse on young adults, such as sleep deprivation and attention deficits, are being increasingly recognized recently. This emerging issue motivated us to analyze the usage patterns related to smartphone overuse. We investigate smartphone usage for 95 college students using surveys, logged data, and interviews. We first divide the participants into risk and non-risk groups based on self-reported rating scale for smartphone overuse. We then analyze the usage data to identify between-group usage differences, which ranged from the overall usage patterns to appspecific usage patterns. Compared with the non-risk group, our results show that the risk group has longer usage time per day and different diurnal usage patterns. Also, the risk group users are more susceptible to push notifications, and tend to consume more online content. We characterize the overall relationship between usage features and smartphone overuse using analytic modeling and provide detailed illustrations of problematic usage behaviors based on interview data.

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An integrated framework for suicide risk prediction

August 20, 2013 Comments off

An integrated framework for suicide risk prediction
Source: KDD ’13 Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining

Suicide is a major concern in society. Despite of great attention paid by the community with very substantive medico-legal implications, there has been no satisfying method that can reliably predict the future attempted or completed suicide. We present an integrated machine learning framework to tackle this challenge. Our proposed framework consists of a novel feature extraction scheme, an embedded feature selection process, a set of risk classifiers and finally, a risk calibration procedure. For temporal feature extraction, we cast the patient’s clinical history into a temporal image to which a bank of one-side filters are applied. The responses are then partly transformed into mid-level features and then selected in l1-norm framework under the extreme value theory. A set of probabilistic ordinal risk classifiers are then applied to compute the risk probabilities and further re-rank the features. Finally, the predicted risks are calibrated. Together with our Australian partner, we perform comprehensive study on data collected for the mental health cohort, and the experiments validate that our proposed framework outperforms risk assessment instruments by medical practitioners.

Design for Forgetting: Disposing of Digital Possessions After a Breakup

May 10, 2013 Comments off

Design for Forgetting: Disposing of Digital Possessions After a Breakup (PDF)
Source: Association for Computing Machinery (ACM)

People are increasingly acquiring huge collections of digital possessions. Despite some pleas for ‘forgetting’, most theorists argue for retaining all these possessions to enhance ‘total recall’ of our everyday lives. However, there has been little exploration of the negative role of digital possessions when people want to forget aspects of their lives. We report on interviews with 24 people about their possessions after a romantic breakup. We found that digital possessions were often evocative and upsetting in this context, leading to distinct disposal strategies with different outcomes. We advance theory by finding strong evidence for the value of intentional forgetting and provide new data about complex practices associated with the disposal of digital possessions. Our findings led to a number of design implications to help people better manage this process, including automatic harvesting of digital possessions, tools for self- ontrol, artifact crafting as sense-making, and digital spaces for shared possessions.

A Longitudinal Study of Follow Predictors on Twitter

May 6, 2013 Comments off

A Longitudinal Study of Follow Predictors on Twitter (PDF)
Source: Association for Computing Machinery (ACM)

Follower count is important to Twitter users: it can indicate popularity and prestige. Yet, holistically, little is understood about what factors – like social behavior, message content, and network structure – lead to more followers. Such information could help technologists design and build tools that help users grow their audiences. In this paper, we study 507 Twitter users and a half-million of their tweets over 15 months. Marrying a longitudinal approach with a negative binomial auto-regression model, we find that variables for message content, social behavior, and network structure should be given equal consideration when predicting link formations on Twitter. To our knowledge, this is the first longitudinal study of follow predictors, and the first to show that the relative contributions of social behavior and message content are just as impactful as factors related to social network structure for predicting growth of online social networks. We conclude with practical and theoretical implications for designing social media technologies.

See: How to get more followers on Twitter (EurekAlert!)

Whom Should I Follow? Identifying Relevant Users During Crises

March 21, 2013 Comments off

Whom Should I Follow? Identifying Relevant Users During Crises

Source: 24th ACM Conference on Hypertext and Social Media (via Arizona State University)

Social media is gaining popularity as a medium of communication before, during, and after crises. In several recent disasters, it has become evident that social media sites like Twitter and Facebook are an important source of information, and in cases they have even assisted in relief e orts. We propose a novel approach to identify a subset of active users during a crisis who can be tracked for fast access to information. Using a Twitter dataset that consists of 12.9 million tweets from 5 countries that are part of the "Arab Spring" movement, we show how instant information access can be achieved by user identification along two dimensions: user’s location and the user’s affi nity towards topics of discussion. Through evaluations, we demonstrate that users selected by our approach generate more information and the quality of the information is better than that of users identified using state-of-the-art techniques.

Hat tip: ResearchBuzz

Phrases That Signal Workplace Hierarchy

February 18, 2012 Comments off
Source:  2012 ACM Conference on Computer Supported Cooperative Work (CSCW)
Hierarchy fundamentally shapes how we act at work. In this paper, we explore the relationship between the words people write in workplace email and the rank of the email’s recipient. Using the Enron corpus as a dataset, we perform a close study of the words and phrases people send to those above them in the corporate hierarchy versus those at the same level or lower. We find that certain words and phrases are strong predictors. For example, “thought you would” strongly suggests that the recipient outranks the sender, while “let’s discuss” implies the opposite. We also find that the phrases people write to their bosses do not demonstrate cognitive processes as often as the ones they write to others. We conclude this paper by interpreting our results and announcing the release of the predictive phrases as a public dataset, perhaps enabling a new class of status-aware applications.

See: Email Language Tips Off Work Hierarchy (Science Daily)


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