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

$1.00 per RT #BostonMarathon #PrayForBoston: analyzing fake content on Twitter

This study found that 29% of the most viral content on Twitter during the Boston bombing crisis were rumors and fake content.AbstractOnline social media has emerged as one of the prominent channels for dissemination of information during real world events. Malicious content is posted online during events, which can result in damage, chaos and monetary losses in the real world. We analyzed one such media i.e. Twitter, for content generated during the event of Boston Marathon Blasts, that occurred on April, 15th, 2013. A lot of fake content and malicious profiles originated on Twitter network during this event. The aim of this work is to perform in-depth characterization of what factors influenced in malicious content and profiles becoming viral. Our results showed that 29% of the most viral content on Twitter, during the Boston crisis were rumors and fake content; while 51% was generic opinions and comments; and rest was true information. We found that large number of users with high social reputation and verified accounts were responsible for spreading the fake content. Next, we used regression prediction model, to verify that, overall impact of all users who propagate the fake content at a given time, can be used to estimate the growth of that content in future. Many malicious accounts were created on Twitter during the Boston event, that were later suspended by Twitter. We identified over six thousand such user profiles, we observed that the creation of such profiles surged considerably right after the blasts occurred. We identified closed community structure and star formation in the interaction network of these suspended profiles amongst themselves

A study on Analysis and Utilization of Crowd-sourced Spatio-temporal Contexts from Social Media

兵庫県立大学大学院201

Detecting Traffic Information From Social Media Texts With Deep Learning Approaches

Mining traffic-relevant information from social media data has become an emerging topic due to the real-time and ubiquitous features of social media. In this paper, we focus on a specific problem in social media mining which is to extract traffic relevant microblogs from Sina Weibo, a Chinese microblogging platform. It is transformed into a machine learning problem of short text classification. First, we apply the continuous bag-of-word model to learn word embedding representations based on a data set of three billion microblogs. Compared to the traditional one-hot vector representation of words, word embedding can capture semantic similarity between words and has been proved effective in natural language processing tasks. Next, we propose using convolutional neural networks (CNNs), long short-term memory (LSTM) models and their combination LSTM-CNN to extract traffic relevant microblogs with the learned word embeddings as inputs. We compare the proposed methods with competitive approaches, including the support vector machine (SVM) model based on a bag of n-gram features, the SVM model based on word vector features, and the multi-layer perceptron model based on word vector features. Experiments show the effectiveness of the proposed deep learning approaches

Localized Events in Social Media Streams: Detection, Tracking, and Recommendation

From the recent proliferation of social media channels to the immense amount of user-generated content, an increasing interest in social media mining is currently being witnessed. Messages continuously posted via these channels report a broad range of topics from daily life to global and local events. As a consequence, this has opened new opportunities for mining event information crucial in many application domains, especially in increasing the situational awareness in critical scenarios. Interestingly, many of these messages are enriched with location information, due to the wide- spread of mobile devices and the recent advancements of today’s location acquisition techniques. This enables location-aware event mining, i.e., the detection and tracking of localized events. In this thesis, we propose novel frameworks and models that digest social media content for localized event detection, tracking, and recommendation. We first develop KeyPicker, a framework to extract and score event-related keywords in an online fashion, accounting for high levels of noise, temporal heterogeneity and outliers in the data. Then, LocEvent is proposed to incrementally detect and track events using a 4-stage procedure. That is, LocEvent receives the keywords extracted by KeyPicker, identifies local keywords, spatially clusters them, and finally scores the generated clusters. For each detected event, a set of descriptive keywords, a location, and a time interval are estimated at a fine-grained resolution. In addition to the sparsity of geo-tagged messages, people sometimes post about events far away from an event’s location. Such spatial problems are handled by novel spatial regularization techniques, namely, graph- and gazetteer-based regularization. To ensure scalability, we utilize a hierarchical spatial index in addition to a multi-stage filtering procedure that gradually suppresses noisy words and considers only event-related ones for complex spatial computations. As for recommendation applications, we propose an event recommender system built upon model-based collaborative filtering. Our model is able to suggest events to users, taking into account a number of contextual features including the social links between users, the topical similarities of events, and the spatio-temporal proximity between users and events. To realize this model, we employ and adapt matrix factorization, which allows for uncovering latent user-event patterns. Our proposed features contribute to directing the learning process towards recommendations that better suit the taste of users, in particular when new users have very sparse (or even no) event attendance history. To evaluate the effectiveness and efficiency of our proposed approaches, extensive comparative experiments are conducted using datasets collected from social media channels. Our analysis of the experimental results reveals the superiority and advantages of our frameworks over existing methods in terms of the relevancy and precision of the obtained results