Local News And Event Detection In Twitter

Twitter, one of the most popular micro-blogging services, allows users to publish short messages on a wide variety of subjects such as news, events, stories, ideas, and opinions, called tweets. The popularity of Twitter, to some extent, arises from its capability of letting users promptly and conveniently contribute tweets to convey diverse information. Specifically, with people discussing what is happening outside in the real world by posting tweets, Twitter captures invaluable information about real-world news and events, spanning a wide scale from large national or international stories like a presidential election to small local stories such as a local farmers market. Detecting and extracting small news and events for a local place is a challenging problem and is the focus of this thesis. In particular, we explore several directions to extract and detect local news and events using tweets in Twitter: a) how to identify local influential people on Twitter for potential news seeders; b) how to recognize unusualness in tweet volume as signals of potential local events; c) how to overcome the data sparsity of local tweets to detect more and smaller undergoing local news and events. Additionally, we also try to uncover implicit correlations between location, time, and text in tweets by learning embeddings for them using a universal representation under the same semantic space. In the first part, we investigate how to measure the spatial influence of Twitter users by their interactions and thereby identify the locally influential users, which we found are usually good news and event seeders in practice. In order to do this, we built a large-scale directed interaction graph of Twitter users. Such a graph allows us to exploit PageRank based ranking procedures to select top local influential people after innovatively incorporating in geographical distance to the transition matrix used for the random walking. In the second part, we study how to recognize the unusualness in tweet volume at a local place as signals of potential ongoing local events. The intuition is that if there is suddenly an abnormal change in the number of tweets at a location (e.g., a significant increase), it may imply a potential local event. We, therefore, present DeLLe, a methodology for automatically Detecting Latest Local Events from geotagged tweet streams (i.e., tweets that contain GPS points). With the help of novel spatiotemporal tweet count prediction models, DeLLe first finds unusual locations which have aggregated an unexpected number of tweets in the latest time period and then calculates, for each such unusual location, a ranking score to identify the ones most likely to have ongoing local events by addressing the temporal burstiness, spatial business, and topical coherence. In the third part, we explore how to overcome the data sparsity of local tweets when trying to discover more and smaller local news or events. Local tweets are those whose locations fall inside a local place. They are very sparse in Twitter, which hinders the detection of small local news or events that have only a handful of tweets. A system, called Firefly, is proposed to enhance the local live tweet stream by tracking the tweets of a large body of local people, and further perform a locality-aware keyword based clustering for event detection. The intuition is that local tweets are published by local people, and tracking their tweets naturally yields a source of local tweets. However, in practice, only 20% Twitter users provide information about where they come from. Thus, a social network-based geotagging procedure is subsequently proposed to estimate locations for Twitter users whose locations are missing. Finally, in order to discover correlations between location, time and text in geotagged tweets, e.g., “find which locations are mostly related to the given topics“ and “find which locations are similar to a given location“, we present LeGo, a methodology for Learning embeddings of Geotagged tweets with respect to entities such as locations, time units (hour-of-day and day-of-week) and textual words in tweets. The resulting compact vector representations of these entities hence make it easy to measure the relatedness between locations, time and words in tweets. LeGo comprises two working modes: crossmodal search (LeGo-CM) and location-similarity search (LeGo-LS), to answer these two types of queries accordingly. In LeGo-CM, we first build a graph of entities extracted from tweets in which each edge carries the weight of co-occurrences between two entities. The embeddings of graph nodes are then learned in the same latent space under the guidance of approximating stationary residing probabilities between nodes which are computed using personalized random walk procedures. In comparison, we supplement edges between locations in LeGo-LS to address their underlying spatial proximity and topic likeliness to support location-similarity search queries

Twitterによるイベントの位置推定

University of Tokyo(東京大学

On the Accuracy of Hyper-local Geotagging of Social Media Content

Social media users share billions of items per year, only a small fraction of which is geotagged. We present a data- driven approach for identifying non-geotagged content items that can be associated with a hyper-local geographic area by modeling the location distributions of hyper-local n-grams that appear in the text. We explore the trade-off between accuracy, precision and coverage of this method. Further, we explore differences across content received from multiple platforms and devices, and show, for example, that content shared via different sources and applications produces significantly different geographic distributions, and that it is best to model and predict location for items according to their source. Our findings show the potential and the bounds of a data-driven approach to geotag short social media texts, and offer implications for all applications that use data-driven approaches to locate content.Comment: 10 page

SURGE: Continuous Detection of Bursty Regions Over a Stream of Spatial Objects

With the proliferation of mobile devices and location-based services, continuous generation of massive volume of streaming spatial objects (i.e., geo-tagged data) opens up new opportunities to address real-world problems by analyzing them. In this paper, we present a novel continuous bursty region detection problem that aims to continuously detect a bursty region of a given size in a specified geographical area from a stream of spatial objects. Specifically, a bursty region shows maximum spike in the number of spatial objects in a given time window. The problem is useful in addressing several real-world challenges such as surge pricing problem in online transportation and disease outbreak detection. To solve the problem, we propose an exact solution and two approximate solutions, and the approximation ratio is $\frac{1-\alpha}{4}$ in terms of the burst score, where $\alpha$ is a parameter to control the burst score. We further extend these solutions to support detection of top-$k$ bursty regions. Extensive experiments with real-world data are conducted to demonstrate the efficiency and effectiveness of our solutions

INRISCO: INcident monitoRing in Smart COmmunities

Major advances in information and communication technologies (ICTs) make citizens to be considered as sensors in motion. Carrying their mobile devices, moving in their connected vehicles or actively participating in social networks, citizens provide a wealth of information that, after properly processing, can support numerous applications for the benefit of the community. In the context of smart communities, the INRISCO [1] proposal intends for (i) the early detection of abnormal situations in cities (i.e., incidents), (ii) the analysis of whether, according to their impact, those incidents are really adverse for the community; and (iii) the automatic actuation by dissemination of appropriate information to citizens and authorities. Thus, INRISCO will identify and report on incidents in traffic (jam, accident) or public infrastructure (e.g., works, street cut), the occurrence of specific events that affect other citizens' life (e.g., demonstrations, concerts), or environmental problems (e.g., pollution, bad weather). It is of particular interest to this proposal the identification of incidents with a social and economic impact, which affects the quality of life of citizens.This work was supported in part by the Spanish Government through the projects INRISCO under Grant TEC2014-54335-C4-1-R, Grant TEC2014-54335-C4-2-R, Grant TEC2014-54335-C4-3-R, and Grant TEC2014-54335-C4-4-R, in part by the MAGOS under Grant TEC2017-84197-C4-1-R, Grant TEC2017-84197-C4-2-R, and Grant TEC2017-84197-C4-3-R, in part by the European Regional Development Fund (ERDF), and in part by the Galician Regional Government under agreement for funding the Atlantic Research Center for Information and Communication Technologies (AtlantTIC)

Can we predict a riot? Disruptive event detection using Twitter

In recent years, there has been increased interest in real-world event detection using publicly accessible data made available through Internet technology such as Twitter, Facebook, and YouTube. In these highly interactive systems, the general public are able to post real-time reactions to “real world” events, thereby acting as social sensors of terrestrial activity. Automatically detecting and categorizing events, particularly small-scale incidents, using streamed data is a non-trivial task but would be of high value to public safety organisations such as local police, who need to respond accordingly. To address this challenge, we present an end-to-end integrated event detection framework that comprises five main components: data collection, pre-processing, classification, online clustering, and summarization. The integration between classification and clustering enables events to be detected, as well as related smaller-scale “disruptive events,” smaller incidents that threaten social safety and security or could disrupt social order. We present an evaluation of the effectiveness of detecting events using a variety of features derived from Twitter posts, namely temporal, spatial, and textual content. We evaluate our framework on a large-scale, real-world dataset from Twitter. Furthermore, we apply our event detection system to a large corpus of tweets posted during the August 2011 riots in England. We use ground-truth data based on intelligence gathered by the London Metropolitan Police Service, which provides a record of actual terrestrial events and incidents during the riots, and show that our system can perform as well as terrestrial sources, and even better in some cases

Public Health

Twitter, crowdsourcing, and other medical technology inventions producing real-time geolocated streams of personalized data have changed the way we think about health (Kostkova 2015). However, Twitter’s strength is its two-way communication nature – both as a health information source but also as a central hub for the creation and dissemination of media health coverage. Health authorities, insurance companies, marketing agencies, and individuals can leverage the availability of large datasets from Twitter to improve early warning services and preparedness, aid disease prevalence mapping, and provide personal targeted health advice, as well as in"uence public sentiment about major health interventions. However, despite the growing potential, there are still many challenges to address to develop robust and reliable systems integrating Twitter streams to real-world provision of healthcare

Geo-Information Harvesting from Social Media Data

As unconventional sources of geo-information, massive imagery and text messages from open platforms and social media form a temporally quasi-seamless, spatially multi-perspective stream, but with unknown and diverse quality. Due to its complementarity to remote sensing data, geo-information from these sources offers promising perspectives, but harvesting is not trivial due to its data characteristics. In this article, we address key aspects in the field, including data availability, analysis-ready data preparation and data management, geo-information extraction from social media text messages and images, and the fusion of social media and remote sensing data. We then showcase some exemplary geographic applications. In addition, we present the first extensive discussion of ethical considerations of social media data in the context of geo-information harvesting and geographic applications. With this effort, we wish to stimulate curiosity and lay the groundwork for researchers who intend to explore social media data for geo-applications. We encourage the community to join forces by sharing their code and data.Comment: Accepted for publication IEEE Geoscience and Remote Sensing Magazin