The use of microblogging for field-based scientific research

Documenting the context in which data are collected is an integral part of the scientific research lifecycle. In field-based research, contextual information provides a detailed description of scientific practices and thus enables data interpretation and reuse. For field data, losing contextual information often means losing the data altogether. Yet, documenting the context of distributed, collaborative, field-based research can be a significant challenge due to the unpredictable nature of real-world settings and to the high degree of variability in data collection methods and scientific practices of different researchers. In this article, we propose the use of microblogging as a mechanism to support collection, ingestion, and publication of contextual information about the variegated digital artifacts that are produced in field research. We perform interviews with scholars involved in field-based environmental and urban sensing research, to determine the extent of adoption of Twitter and similar microblogging platforms and their potential use for field-specific research applications. Based on the results of these interviews as well as participant observation of field activities, we present the design, development, and pilot evaluation of a microblogging application integrated with an existing data collection platform on a handheld device. We investigate whether microblogging accommodates the variable and unpredictable nature of highly mobile research and whether it represents a suitable mechanism to document the context of field research data early in the scientific information lifecycle.Comment: Proceedings of the 45th Hawaii International Conference on System Science (HICSS-45 2012

Smartphone sensing platform for emergency management

The increasingly sophisticated sensors supported by modern smartphones open up novel research opportunities, such as mobile phone sensing. One of the most challenging of these research areas is context-aware and activity recognition. The SmartRescue project takes advantage of smartphone sensing, processing and communication capabilities to monitor hazards and track people in a disaster. The goal is to help crisis managers and members of the public in early hazard detection, prediction, and in devising risk-minimizing evacuation plans when disaster strikes. In this paper we suggest a novel smartphone-based communication framework. It uses specific machine learning techniques that intelligently process sensor readings into useful information for the crisis responders. Core to the framework is a content-based publish-subscribe mechanism that allows flexible sharing of sensor data and computation results. We also evaluate a preliminary implementation of the platform, involving a smartphone app that reads and shares mobile phone sensor data for activity recognition.Comment: 11th International Conference on Information Systems for Crisis Response and Management ISCRAM2014 (2014

Traffic event detection framework using social media

This is an accepted manuscript of an article published by IEEE in 2017 IEEE International Conference on Smart Grid and Smart Cities (ICSGSC) on 18/09/2017, available online: https://ieeexplore.ieee.org/document/8038595 The accepted version of the publication may differ from the final published version.© 2017 IEEE. Traffic incidents are one of the leading causes of non-recurrent traffic congestions. By detecting these incidents on time, traffic management agencies can activate strategies to ease congestion and travelers can plan their trip by taking into consideration these factors. In recent years, there has been an increasing interest in Twitter because of the real-time nature of its data. Twitter has been used as a way of predicting revenues, accidents, natural disasters, and traffic. This paper proposes a framework for the real-time detection of traffic events using Twitter data. The methodology consists of a text classification algorithm to identify traffic related tweets. These traffic messages are then geolocated and further classified into positive, negative, or neutral class using sentiment analysis. In addition, stress and relaxation strength detection is performed, with the purpose of further analyzing user emotions within the tweet. Future work will be carried out to implement the proposed framework in the West Midlands area, United Kingdom.Published versio

Semantic Wide and Deep Learning for Detecting Crisis-Information Categories on Social Media

When crises hit, many flog to social media to share or consume information related to the event. Social media posts during crises tend to provide valuable reports on affected people, donation offers, help requests, advice provision, etc. Automatically identifying the category of information (e.g., reports on affected individuals, donations and volunteers) contained in these posts is vital for their efficient handling and consumption by effected communities and concerned organisations. In this paper, we introduce Sem-CNN; a wide and deep Convolutional Neural Network (CNN) model designed for identifying the category of information contained in crisis-related social media content. Unlike previous models, which mainly rely on the lexical representations of words in the text, the proposed model integrates an additional layer of semantics that represents the named entities in the text, into a wide and deep CNN network. Results show that the Sem-CNN model consistently outperforms the baselines which consist of statistical and non-semantic deep learning models

Men, Women, Microblogging: Where Do We Stand?

With millions of users worldwide, microblogging has developed into a powerful tool for interaction and information dissemination. While both men and women readily use this technology, there are significant differences in how they embrace it. Understanding these differences is important to ensure gender parity, provide advertisers with actionable insights on the marketing potential of both groups, and to inform current theories on how microblogging affordances shape gender roles. So far, existing research has not provided a unified framework for such analysis, with gender insights scattered across multiple studies. To fill this gap, our study conducts a comprehensive meta-review of existing research. We find that current discourse offers a solid body of knowledge on gender differences in adoption, shared content, stylistic presentation, and a rather convoluted picture of female and male interaction. Together, our structured findings offer a deeper insight into the underlying dynamics of gender differences in microblogging

Combining Machine Learning Techniques and Natural Language Processing to Infer Emotions Using Spanish Twitter Corpus

Proceedings of: 11th Conference on Practical Applications of Agents and Multi-Agent Systems (PAAMS 13). Salamanca, Spain, May 22-24, 2013.In the recent years, microblogging services, as Twitter, have become a popular tool for expressing feelings, opinions, broadcasting news, and communicating with friends. Twitter users produced more than 340 million tweets per day which may be consider a rich source of user information. We take a supervised approach to the problem, but leverage existing hashtags in Twitter for building our training data. Finally, we tested the Spanish emotional corpus applying two different machine learning algorithms for emotion identification reaching about 65% accuracy.This work was supported in part by Projects MEyC TEC2012-37832-C02-01, MEyC TEC2011-28626-C02-02 and CAM CONTEXTS (S2009/TIC-1485)Publicad

Adoption of Micro-blogging (Twitter) by Various Learner Types in an Information Systems unit: An Exploratory Study

A major obstacle in the practice of e-learning is the limited understanding of learners’ characteristics and perceptions about technology use. In this case, understanding the relationship between learning styles and Twitter usage could help educators to design better instructional strategies. This would also lead to better student experience and improved learning outcomes. Hence, in this study we investigate learning styles of an Information Systems undergraduate class and its influence on the use of micro-blogging (Twitter). The end of semester survey reveals that the majority of students were “well-balanced” on all learning style scales except ‘visual-verbal’ scale where visuals outclassed verbals. More importantly, active and visual learners emerged as the most significant adopters of Twitter. The study has implications for educators who wish to accommodate their students’ learning preferences and to enhance Web 2.0 usage in their teaching, in particular micro-blogging

Social media networks: Rich on-line data sources

This chapter illustrates how social media networks can be harnessed for research to highlight feelings, behaviour and opinions of customers. This is a new area of research and will include discussions on data mining and thematic analysis