17,926 research outputs found
Social Media Analytics in Disaster Response: A Comprehensive Review
Social media has emerged as a valuable resource for disaster management,
revolutionizing the way emergency response and recovery efforts are conducted
during natural disasters. This review paper aims to provide a comprehensive
analysis of social media analytics for disaster management. The abstract begins
by highlighting the increasing prevalence of natural disasters and the need for
effective strategies to mitigate their impact. It then emphasizes the growing
influence of social media in disaster situations, discussing its role in
disaster detection, situational awareness, and emergency communication. The
abstract explores the challenges and opportunities associated with leveraging
social media data for disaster management purposes. It examines methodologies
and techniques used in social media analytics, including data collection,
preprocessing, and analysis, with a focus on data mining and machine learning
approaches. The abstract also presents a thorough examination of case studies
and best practices that demonstrate the successful application of social media
analytics in disaster response and recovery. Ethical considerations and privacy
concerns related to the use of social media data in disaster scenarios are
addressed. The abstract concludes by identifying future research directions and
potential advancements in social media analytics for disaster management. The
review paper aims to provide practitioners and researchers with a comprehensive
understanding of the current state of social media analytics in disaster
management, while highlighting the need for continued research and innovation
in this field.Comment: 11 page
The Development of a Temporal Information Dictionary for Social Media Analytics
Dictionaries have been used to analyze text even before the emergence of social media and the use of dictionaries for sentiment analysis there. While dictionaries have been used to understand the tonality of text, so far it has not been possible to automatically detect if the tonality refers to the present, past, or future. In this research, we develop a dictionary containing time-indicating words in a wordlist (T-wordlist). To test how the dictionary performs, we apply our T-wordlist on different disaster related social media datasets. Subsequently we will validate the wordlist and results by a manual content analysis. So far, in this research-in-progress, we were able to develop a first dictionary and will also provide some initial insight into the performance of our wordlist
Text Analytics for Android Project
Most advanced text analytics and text mining tasks include text classification, text clustering, building ontology, concept/entity extraction, summarization, deriving patterns within the structured data, production of granular taxonomies, sentiment and emotion analysis, document summarization, entity relation modelling, interpretation of the output. Already existing text analytics and text mining cannot develop text material alternatives (perform a multivariant design), perform multiple criteria analysis,
automatically select the most effective variant according to different aspects (citation index of papers (Scopus, ScienceDirect, Google Scholar) and authors (Scopus, ScienceDirect, Google Scholar), Top 25 papers, impact factor of journals, supporting phrases, document name and contents, density of keywords), calculate utility degree and market value. However, the Text Analytics for Android Project can perform the aforementioned functions. To the best of the knowledge herein, these functions have not been previously implemented; thus this is the first attempt to do so. The Text Analytics for Android Project is briefly described in this article
Cultivating Lifelong Donors: Stewardship and the Fundraising Pyramid
This handbook helps nonprofits build long-term giving programs that span the entire supporter lifecycle, from engagement through the end of life. It highlights strategies for engaging new supporters online, investigates the characteristics of loyal donors, examines the importance of developing personal relationships with transitional giving prospects, and discusses donor cultivation
Recommended from our members
Tracing the German Centennial Flood in the Stream of Tweets: First Lessons Learned
Social microblogging services such as Twitter result in massive streams of georeferenced messages and geolocated status updates. This real-time source of information is invaluable for many application areas, in particular for disaster detection and response scenarios. Consequently, a considerable number of works has dealt with issues of their acquisition, analysis and visualization. Most of these works not only assume an appropriate percentage of georeferenced messages that allows for detecting relevant events for a specific region and time frame, but also that these geolocations are reasonably correct in representing places and times of the underlying spatio-temporal situation. In this paper, we review these two key assumption based on the results of applying a visual analytics approach to a dataset of georeferenced Tweets from Germany over eight months witnessing several large-scale flooding situations throughout the country. Our results con rm the potential of Twitter as a distributed 'social sensor' but at the same time highlight some caveats in interpreting immediate results. To overcome these limits we explore incorporating evidence from other data sources including further social media and mobile phone network metrics to detect, confirm and refine events with respect to location and time. We summarize the lessons learned from our initial analysis by proposing recommendations and outline possible future work directions
Crisis Analytics: Big Data Driven Crisis Response
Disasters have long been a scourge for humanity. With the advances in
technology (in terms of computing, communications, and the ability to process
and analyze big data), our ability to respond to disasters is at an inflection
point. There is great optimism that big data tools can be leveraged to process
the large amounts of crisis-related data (in the form of user generated data in
addition to the traditional humanitarian data) to provide an insight into the
fast-changing situation and help drive an effective disaster response. This
article introduces the history and the future of big crisis data analytics,
along with a discussion on its promise, challenges, and pitfalls
- …