Crowdsourced Exploration of Mobile App Features: A Case Study of the Fort McMurray Wildfire

The ubiquity of mobile devices has led to unprecedented growth in not only the usage of apps, but also their capacity to meet people's needs. Smart phones take on a heightened role in emergency situations, as they may suddenly be among their owner's only possessions and resources. The 2016 wildfire in Fort McMurray, Canada, intrigued us to study the functionality of the existing apps by analyzing social media information. We investigated a method to suggest features that are useful for emergency apps. Our proposed method called MAPFEAT, combines various machine learning techniques to analyze tweets in conjunction with crowdsourcing and guides an extended search in app stores to find currently missing features in emergency apps based on the needs stated in social media. MAPFEAT is evaluated by a real-world case study of the Fort McMurray wildfire, where we analyzed 69,680 unique tweets recorded over a period from May 2nd to May 7th, 2016. We found that (i) existing wildfire apps covered a range of 28 features with not all of them being considered helpful or essential, (ii) a large range of needs articulated in tweets can be mapped to features existing in non-emergency related apps, and (iii) MAPFEAT's suggested feature set is better aligned with the needs expressed by general public. Only six of the features existing in wildfire apps is among top 40 crowdsourced features explored by MAPFEAT, with the most important one just ranked 13th. By using MAPFEAT, we proactively understand victims' needs and suggest mobile software support to the people impacted. MAPFEAT looks beyond the current functionality of apps in the same domain and extracts features using variety of crowdsourced data.

[1]  Andrew Begel,et al.  Analyze this! 145 questions for data scientists in software engineering , 2013, ICSE.

[2]  Anjali Ganesh Jivani,et al.  A Comparative Study of Stemming Algorithms , 2011 .

[3]  Christian Bird,et al.  Leveraging the Crowd: How 48,000 Users Helped Improve Lync Performance , 2013, IEEE Software.

[4]  Neil A. M. Maiden,et al.  Using Mobile RE Tools to Give End-Users Their Own Voice , 2010, 2010 18th IEEE International Requirements Engineering Conference.

[5]  Leysia Palen,et al.  Online public communications by police & fire services during the 2012 Hurricane Sandy , 2014, CHI.

[6]  Yikun Liu,et al.  Top health trends: An information visualization tool for awareness of local health trends , 2013, ISCRAM.

[7]  Ning Chen,et al.  AR-miner: mining informative reviews for developers from mobile app marketplace , 2014, ICSE.

[8]  Zhang Dong,et al.  Analysis and research on microblogging network model based on crawler data , 2011, Proceedings of 2011 International Conference on Computer Science and Network Technology.

[9]  Jane Cleland-Huang,et al.  On-demand feature recommendations derived from mining public product descriptions , 2011, 2011 33rd International Conference on Software Engineering (ICSE).

[10]  David Ratcliffe,et al.  Finding Fires with Twitter , 2013, ALTA.

[11]  William Ribarsky,et al.  LeadLine: Interactive visual analysis of text data through event identification and exploration , 2012, 2012 IEEE Conference on Visual Analytics Science and Technology (VAST).

[12]  Sotiris B. Kotsiantis,et al.  Supervised Machine Learning: A Review of Classification Techniques , 2007, Informatica.

[13]  Leysia Palen,et al.  Twitter adoption and use in mass convergence and emergency events , 2009 .

[14]  Steven Bird,et al.  NLTK: The Natural Language Toolkit , 2002, ACL.

[15]  Leysia Palen,et al.  Microblogging during two natural hazards events: what twitter may contribute to situational awareness , 2010, CHI.

[16]  Jane Cleland-Huang,et al.  Supporting Domain Analysis through Mining and Recommending Features from Online Product Listings , 2013, IEEE Transactions on Software Engineering.

[17]  J Brian Houston,et al.  Social media and disasters: a functional framework for social media use in disaster planning, response, and research. , 2015, Disasters.

[18]  Heiko Gewald,et al.  Does Money Matter? Motivational Factors for Participation in Paid- and Non-Profit-Crowdsourcing Communities , 2013, Wirtschaftsinformatik.

[19]  Brian D. Davison,et al.  Empirical study of topic modeling in Twitter , 2010, SOMA '10.

[20]  Eric Schenk,et al.  Crowdsourcing: What can be Outsourced to the Crowd, and Why ? , 2009 .

[21]  Edson C. Tandoc,et al.  Communicating on Twitter during a disaster: An analysis of tweets during Typhoon Haiyan in the Philippines , 2015, Comput. Hum. Behav..

[22]  Alessandra Gorla,et al.  Checking app behavior against app descriptions , 2014, ICSE.

[23]  Walter Daelemans,et al.  Pattern for Python , 2012, J. Mach. Learn. Res..

[24]  Walid Maalej,et al.  Bug report, feature request, or simply praise? On automatically classifying app reviews , 2015, 2015 IEEE 23rd International Requirements Engineering Conference (RE).

[25]  Mark Harman,et al.  A survey of the use of crowdsourcing in software engineering , 2017, J. Syst. Softw..

[26]  Michael I. Jordan,et al.  Latent Dirichlet Allocation , 2001, J. Mach. Learn. Res..

[27]  Edward A. Fox,et al.  PhaseVis1: What, when, where, and who in visualizing the four phases of emergency management through the lens of social media , 2013, ISCRAM.

[28]  David S. Ebert,et al.  Spatiotemporal social media analytics for abnormal event detection and examination using seasonal-trend decomposition , 2012, 2012 IEEE Conference on Visual Analytics Science and Technology (VAST).

[29]  Danah Boyd,et al.  Tweet, Tweet, Retweet: Conversational Aspects of Retweeting on Twitter , 2010, 2010 43rd Hawaii International Conference on System Sciences.

[30]  Maleknaz Nayebi,et al.  Toward Data-Driven Requirements Engineering , 2016, IEEE Software.

[31]  Yuanyuan Zhang,et al.  Feature lifecycles as they spread, migrate, remain, and die in App Stores , 2015, 2015 IEEE 23rd International Requirements Engineering Conference (RE).

[32]  Thomas Ertl,et al.  Spatiotemporal anomaly detection through visual analysis of geolocated Twitter messages , 2012, 2012 IEEE Pacific Visualization Symposium.

[33]  Yuanyuan Zhang,et al.  App store mining and analysis: MSR for app stores , 2012, 2012 9th IEEE Working Conference on Mining Software Repositories (MSR).

[34]  Alok N. Choudhary,et al.  Twitter Trending Topic Classification , 2011, 2011 IEEE 11th International Conference on Data Mining Workshops.

[35]  Yuanyuan Zhang,et al.  A Survey of App Store Analysis for Software Engineering , 2017, IEEE Transactions on Software Engineering.

[36]  Jie Yin,et al.  Using Social Media to Enhance Emergency Situation Awareness , 2012, IEEE Intelligent Systems.