On the locality of keywords in Twitter streams

Abstract

The continuously increasing popularity of social media sites such as Twitter and Facebook has recently led to a number of approaches to detect and extract event information from social media streams. Such events play an important role, e.g., in supporting location-based services and improving situational awareness. Moreover, the introduction of GPSequipped communication devises has led to an increase in the percentage of geo-tagged messages. These help to detect localized events, i.e., events occurring at a certain location, such as sport events or accidents. The main entities that indicate a localized event are local keywords that exhibit a surge in usage at the event location.In this paper, we propose an approach to extract local keywords from a Twitter stream by (1) identifying local keywords, and (2) estimating the central location of each keyword. This extraction process is performed in an online fashion using a sliding window on the Twitter stream. In addition, we address the problem of spatial outliers that adversely affect a proper identification of local keywords. Outliers occur when people far away from an event location use related keywords in their Tweets. We handle this problem by adjusting the spatial distribution of keywords based on their co-occurrence with place names that may refer to the location of an event. We evaluate the performance of our framework to reliably and efficiently extracting local keywords and estimating their central locations using a Twitter dataset.

Early Flood Detection for Rapid Humanitarian Response: Harnessing Near Real-Time Satellite and Twitter Signals

“…Whereas satellite information has a typical delay of 24 h or more, social media messages can already be accessed within minutes of publication. Recent advances have made it possible to attribute social media information to geographic locations by extracting the body text (e.g., "New York City") and linking this to the location on a map [15][16][17]. One of the first times that social media was used on a large scale for disaster monitoring was after the earthquake in Haiti in 2010 [18].…”

Big Spatial Data Management for the Internet of Things: A Survey

“…Dynamic smart city and Industry 4.0 application scenarios that require intermixing loads in an unprecedented mashup fashion are innumerable including, for example, for road traffic control [49], clustering microblogging topics by region [50]. The aspect that is axiomatic in all those scenarios is that they necessitate various spatial analytics.…”

Top-k term publish/subscribe for geo-textual data streams

“…Jonathan et al [31] study the problem of finding top-k trending terms within an arbitrary subset of documents selected based on their attributes. Additionally, Ab-delhaq et al [1,2] focus on extracting local keywords from a Twitter stream by identifying local keywords and estimating the central location of each keyword. Wang et al [59] identify local top-k maximal frequent keyword co-occurrence patterns over streams of geo-tagged tweets.…”

“…Specifically, RST subscriptions continuously maintain the top-k most popular trending terms within a user-defined region. This kind of subscription relies on a temporal popularity score that quantifies the popularity of a term by taking the following two aspects into account: (1) The frequency of the term in documents published in the subscription region;…”

“…To address these challenges, we propose an approach that exploits the following techniques to process RST and SST subscriptions. 1 We say a subscription matches a term if the term is a top-k result of the subscription Fig. 1 Framework for processing SST and RST subscriptions (1) We propose approaches that enable efficient computation of real-time top-k result updates of SST and RST subscriptions by avoiding the popularity score recomputation of each term in a subscription, and thus reduce the time complexity (Sections 3.2 and 4.3).…”