Active Online Learning for Social Media Analysis to Support Crisis Management

People use social media (SM) to describe and discuss different situations they are involved in, like crises. It is therefore worthwhile to exploit SM contents to support crisis management, in particular by revealing useful and unknown information about the crises in real-time. Hence, we propose a novel active online multiple-prototype classifier, called AOMPC. It identifies relevant data related to a crisis. AOMPC is an online learning algorithm that operates on data streams and which is equipped with active learning mechanisms to actively query the label of ambiguous unlabeled data. The number of queries is controlled by a fixed budget strategy. Typically, AOMPC accommodates partly labeled data streams. AOMPC was evaluated using two types of data: (1) synthetic data and (2) SM data from Twitter related to two crises, Colorado Floods and Australia Bushfires. To provide a thorough evaluation, a whole set of known metrics was used to study the quality of the results. Moreover, a sensitivity analysis was conducted to show the effect of AOMPC’s parameters on the accuracy of the results. A comparative study of AOMPC against other available online learning algorithms was performed. The experiments showed very good behavior of AOMPC for dealing with evolving, partly labeled data streams

Batch-based Active Learning: Application to Social Media Data for Crisis Management

Classification of evolving data streams is a challenging task, which is suitably tackled with online learning approaches. Data is processed instantly requiring the learning machinery to (self-)adapt by adjusting its model. However for high velocity streams, it is usually difficult to obtain labeled samples to train the classification model. Hence, we propose a novel online batch-based active learning algorithm (OBAL) to perform the labeling. OBAL is developed for crisis management applications where data streams are generated by the social media community. OBAL is applied to discriminate relevant from irrelevant social media items. An emergency management user will be interactively queried to label chosen items. OBAL exploits the boundary items for which it is highly uncertain about their class and makes use of two classifiers: k-Nearest Neighbors (kNN) and Support Vector Machine (SVM). OBAL is equipped with a labeling budget and a set of uncertainty strategies to identify the items for labeling. An extensive analysis is carried out to show OBAL's performance, the sensitivity of its parameters, and the contribution of the individual uncertainty strategies. Two types of datasets are used: synthetic and social media datasets related to crises. The empirical results illustrate that OBAL has a very good discrimination power

Using Case Work as a Pretest to Measure Crisis Leadership Preparedness

Today’s leaders must thrive in a world of turbulence and constant change. Unstable conditions frequently generate crises, emphasizing the need for crisis leadership preparedness, which is missing from many business curricula. Thus, the purpose of this work was to develop a learning module in crisis leadership preparedness. As a baseline measure or pretest, 217 graduate students were asked to analyze two crisis leadership cases during the first week of an entry leadership class. Content analysis provided the method to identify where student analyses fell short. These gaps in learning then informed the creation of student learning objectives. Applying inquiry-based learning, I then suggest instructional methods that I incorporated into an active learning module to better prepare today’s leaders for crisis leadership

Preparing millennials as digital citizens and socially and environmentally responsible business professionals in a socially irresponsible climate

As of 2015, a millennial born in the 1990's became the largest population in the workplace and are still growing. Studies indicate that a millennial is tech savvy but lag in the exercise of digital responsibility. In addition, they are passive towards environmental sustainability and fail to grasp the importance of social responsibility. This paper provides a review of such findings relating to business communications educators in their classrooms. The literature should enable the development of a millennial as an excellent global citizen through business communications curricula that emphasizes digital citizenship, environmental sustainability and social responsibility. The impetus for this work is to provide guidance in the development of courses and teaching strategies customized to the development of each millennial as a digital, environmental and socially responsible global citizen

Engineering Crowdsourced Stream Processing Systems

A crowdsourced stream processing system (CSP) is a system that incorporates crowdsourced tasks in the processing of a data stream. This can be seen as enabling crowdsourcing work to be applied on a sample of large-scale data at high speed, or equivalently, enabling stream processing to employ human intelligence. It also leads to a substantial expansion of the capabilities of data processing systems. Engineering a CSP system requires the combination of human and machine computation elements. From a general systems theory perspective, this means taking into account inherited as well as emerging properties from both these elements. In this paper, we position CSP systems within a broader taxonomy, outline a series of design principles and evaluation metrics, present an extensible framework for their design, and describe several design patterns. We showcase the capabilities of CSP systems by performing a case study that applies our proposed framework to the design and analysis of a real system (AIDR) that classifies social media messages during time-critical crisis events. Results show that compared to a pure stream processing system, AIDR can achieve a higher data classification accuracy, while compared to a pure crowdsourcing solution, the system makes better use of human workers by requiring much less manual work effort

Crisis Communication Patterns in Social Media during Hurricane Sandy

Hurricane Sandy was one of the deadliest and costliest of hurricanes over the past few decades. Many states experienced significant power outage, however many people used social media to communicate while having limited or no access to traditional information sources. In this study, we explored the evolution of various communication patterns using machine learning techniques and determined user concerns that emerged over the course of Hurricane Sandy. The original data included ~52M tweets coming from ~13M users between October 14, 2012 and November 12, 2012. We run topic model on ~763K tweets from top 4,029 most frequent users who tweeted about Sandy at least 100 times. We identified 250 well-defined communication patterns based on perplexity. Conversations of most frequent and relevant users indicate the evolution of numerous storm-phase (warning, response, and recovery) specific topics. People were also concerned about storm location and time, media coverage, and activities of political leaders and celebrities. We also present each relevant keyword that contributed to one particular pattern of user concerns. Such keywords would be particularly meaningful in targeted information spreading and effective crisis communication in similar major disasters. Each of these words can also be helpful for efficient hash-tagging to reach target audience as needed via social media. The pattern recognition approach of this study can be used in identifying real time user needs in future crises

Social Media for Cities, Counties and Communities

Social media (i.e., Twitter, Facebook, Flickr, YouTube) and other tools and services with user- generated content have made a staggering amount of information (and misinformation) available. Some government officials seek to leverage these resources to improve services and communication with citizens, especially during crises and emergencies. Yet, the sheer volume of social data streams generates substantial noise that must be filtered. Potential exists to rapidly identify issues of concern for emergency management by detecting meaningful patterns or trends in the stream of messages and information flow. Similarly, monitoring these patterns and themes over time could provide officials with insights into the perceptions and mood of the community that cannot be collected through traditional methods (e.g., phone or mail surveys) due to their substantive costs, especially in light of reduced and shrinking budgets of governments at all levels. We conducted a pilot study in 2010 with government officials in Arlington, Virginia (and to a lesser extent representatives of groups from Alexandria and Fairfax, Virginia) with a view to contributing to a general understanding of the use of social media by government officials as well as community organizations, businesses and the public. We were especially interested in gaining greater insight into social media use in crisis situations (whether severe or fairly routine crises, such as traffic or weather disruptions)