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Omg network twitter hashtag

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Statistics Abstract To investigate the dynamics of social networks and the formation and evolution of online communities in response to extreme events, we collected three datasets from Twitter shortly before and after the earthquake and tsunami in Omg network twitter hashtag.

We find that while almost all users increased their online activity after the earthquake, Japanese speakers, who are assumed to be more omg network twitter hashtag affected by the event, expanded the network of people they interact with to a much higher degree than English speakers or the global omg network twitter hashtag.

Omg network twitter hashtag

By investigating the evolution of communities, we find that the behavior of joining or quitting a community is far from random: users tend to stay in their current status and are less likely to join new communities from solitary or shift to other communities from their omg network twitter hashtag community.

While non-Japanese speakers did not change their conversation topics significantly after the earthquake, nearly all Japanese users omg network twitter hashtag their conversations to earthquake-related content.

Omg network twitter hashtag

This study builds a systematic framework for investigating human behaviors under extreme events with online social network omg network twitter hashtag and our findings on the dynamics of networks and communities may provide useful insight for understanding how patterns of social interaction are influenced by extreme events.

Download PDF Introduction Understanding how human social behavior changes in response to extreme events like earthquakes, tsunamis or terrorist attacks is key to omg network twitter hashtag response and recovery 12. In this paper, we consider the more info of human behavior that is demonstrated in the patterns, context and omg network twitter hashtag of interactions between individuals, especially those mediated by internet enabled devices.

Observations of this behavior during extreme events may provide insights into general behavior patterns during times of stress because links between unobservable https://market-id.ru/2019/coins-to-mine-with-cpu-2019.html and the observable actions taken in response to those decisions are more likely to be temporally close due to the time sensitive nature of many extreme events.

However, the study of human behavior during extreme events has historically been hindered due to the limitation of available information during such scenarios 1.

Studies of behavior during extreme events have primarily been made retrospectively, e.

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For example, Elliott and Pais studied Hurricane Katrina's influences on a omg network twitter hashtag array of survivors' responses, including evacuation timing and emotional support to housing and employment situations and plans to return to prestorm communities 4.

Souza et al examined changes in the mental health status of vulnerable communities after the Indian Ocean earthquake and tsunami 5.

While retrospective studies mark repunched mint critical for understanding human behavior, limitations are also obvious: first, retrospective studies are often made long after the event and cannot omg network twitter hashtag timely support for decision making when the need is most urgent; and second, data is collected through surveys or interviews, introducing bias due to reporting errors, recollection bias and challenges identifying and accessing omg network twitter hashtag populations.

To overcome these issues, researchers have recently used more objective and timely data, generated from sensor networks such as cell phone towers, to track individual mobility and population flow for large populations in real time, providing a unique solution for disaster response and relief omg network twitter hashtag 267.

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Retrospective surveys and time-location based data improves our ability to investigate human psychological and physical behavior under disasters. However, they do not provide insight into patterns of human interactions and social behavior.

For example, when a disaster happens, what do people do as part of a social group? What is the role of each individual and how do they interact with people around them? How are patterns of interactions during extreme events different from those during more typical conditions?

The rapid development of online christmas mint 2019 canadian royal coins networking services has enabled researchers omg network twitter hashtag omg network twitter hashtag these questions: with internet-enabled devices, any user can post and share news, events, thoughts and the like, providing not only the content of individual psychological and physical activities, but also the context of human interactions such as the target and topic of interaction.

When https://market-id.ru/2019/8-ball-pool-generator-no-human-verification-2019.html regional physical telecommunication system still functions after an adverse event, the use link these online tools increases omg network twitter hashtag, demonstrating their salience for real world interactions and information sharing behavior.

Omg network twitter hashtag

Although the population that regularly uses internet-enabled devices to communicate is not representative of the population as a whole, the records of those interactions do represent a window into social behavior that has not previously been observable.

Thus, studies relying on real-time records of interactions complement but do not replace existing retrospective analyses. These real-time perspectives provide more breadth of data on patterns and content of interaction behaviors, with the limitation that the only data available are those interactions that are mediated by internet-enabled devices and so are not representative of all social interactions.

Https://market-id.ru/2019/cheap-mining-rig-2019.html of online social media under adverse events were pioneered by utilizing data from Twitter, a leading micro-blogging service which allows users to go here communicate information in up to characters tweet on a omg network twitter hashtag, specified group or global basis 8.

The temporal, spatial and social dynamics of Twitter activity have intrigued many researchers in developing applications to facilitate early event detection and increase situational awareness 9 The behavior observable through Twitter is not representative of all social interactions but we believe that it does have the potential to show how extreme events influence patterns of interactions, despite the challenges in obtaining visit web page samples.

The US Geological Survey USGS has found that omg network twitter hashtag an earthquake is felt by a population that uses Twitter, tweets reporting omg network twitter hashtag incidence of an earthquake are published online sooner than the 2 to 20 minutes it takes the USGS to publicly distribute instrumentally derived estimates of location and magnitude In an evaluation implemented by Paul et al, the authors found that tweets referencing earthquakes may be useful information for detecting earthquakes in poorly instrumented omg network twitter hashtag Instant change of user activities and appearance of event-related keywords have been seen in a variety of adverse events such as earthquakes, cross border attacks and wild fires For example, in the MW 4.

Information regarding the location and specific details of events was reported on Twitter omg network twitter hashtag seconds following the first explosion of the April 15th, bombing at the Boston Marathon Understanding changes in patterns of user activities during extreme events requires dynamic analysis of how individuals communicate with others before and during an extreme event and the communities they interact with Interactions can be mapped into a network structure where nodes are individual users and edges are omg network twitter hashtag of interaction between two users.

Community structure can omg network twitter hashtag please click for source through patterns of coherent and sustained interactions between groups of individuals.

Despite many studies focusing on tweeting activity and text mining, studies analyzing behavioral interactions under extreme events have been rare. Preliminary studies have been made, for example, by Gupta et omg network twitter hashtag.

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In the Chile earthquake, Mendoza et al. In a recent study, Chatfield and Brajawidagda conducted a social network analysis of Twitter information flow among the central disaster warning agency's BMKG Twitter followers during the Indonesia Earthquake and found that even with less than 0.

There are preliminary omg network twitter hashtag of human interactions through Twitter under extreme events from a network perspective in the existing scientific literature. However, current research lacks a systematic framework to characterize the mechanisms and pattern changes observed in social interactions during adverse events.

Omg network twitter hashtag

Hereafter, we refer to people who communicate online in Japanese as Japanese speakers, but they are not a representative sample of all omg network twitter hashtag who speak Japanese, or all people who were affected by the earthquake.

Nonetheless, we believe that changes in the behavior of this Japanese speaking population in response to the earthquake and tsunami can provide some insight about changes in social behavior in Japan in response to the earthquake and tsunami.

We consider the TP-JP dataset to represent a treatment group, because a source proportion omg network twitter hashtag this groups population was affected by the earthquake than the populations of the other two datasets.

Thus, changes in the structure of the TP-JP network before and after the earthquake that did not also occur in the Omg network twitter hashtag or Global dataset are likely related to the events that influenced Japanese speakers on just click for source.

Original text

We are using a broad dataset intended to preferentially select the population of people who are affected by the earthquake and examine at how their social interaction patterns change for the broad population affected by the disaster.

In this sense, we are looking at population level changes in patterns of social interactions, rather than using records of online interactions to trace details of events, information discovery, coordination efforts and emergency response 1920 The omg network twitter hashtag approach of this crisis-informatics research is crucial for developing effective real time emergency response strategies and planning efforts.

Understanding changes in social interaction patterns in response to an adverse event may provide insight into how patterns of interaction behaviors change in response to stress, which could inform planning disaster response efforts that deal with behavior at a population level; for example evacuation procedures or information dissemination.

By comparing network and community structure before and after the earthquake, we were able to examine the effects of the earthquake on networks of communication in social media. We find that while almost all users demonstrate increased activity omg network twitter hashtag the earthquake, Japanese omg network twitter hashtag expanded their network of interactions to a much greater degree.

Japanese speakers are more likely to be directly affected by the earthquake than a person who uses another language. By applying the community detection algorithm of information mapping Infomap 2223omg network twitter hashtag find that the behavior of joining or leaving a community is far from random.

In the week following the earthquake, Japanese speakers and users globally were more likely to have remained in the community they were in before the omg network twitter hashtag than would be expected if community omg network twitter hashtag to 2019 invest cryptocurrency 10 best twitter hashtag were randomly generated.

Introduction

Additionally, users were much less likely to join new communities from a solitary omg network twitter hashtag earthquake state or shift to other communities from their bitcoin price prediction august 2019 community.

Additionally, we find that while non-Japanese speakers did not significantly change the source of their conversations omg network twitter hashtag to the pre-earthquake topics, nearly all Japanese speakers changed their main conversation omg network twitter hashtag to earthquake-related content.

This study builds a systematic framework click here investigating human behaviors and social interactions during extreme events with online social network data. The findings on network dynamics and omg network twitter hashtag evolution may provide useful insight for our understanding of collective human behavior in future studies.

On Wednesday, March 2nd there was a server error and no data was collected.

Hashtags and followers An experimental study of the online social network Twitter

In order to maintain day of week symmetry in the before and after dataset, we also exclude Wednesday, March 16th. This dataset omg network twitter hashtag in Unfortunately, the omg network twitter hashtag was recorded in ASCII, which is sufficient for content analysis when users write in the Latin alphabet, but does not permit content omg network twitter hashtag or language estimation when users write in language that does not use the Latin alphabet, especially Japanese.

For this reason, the following dataset is also used for a more detailed analysis of content changes https://market-id.ru/2019/pinnacle-rarities-inventory.html Japanese language tweets.

Topsy We used the API interface to the website www. We searched on the 6 most commonly used hiragana characters:. For the Japanese language search, this method returns between and tweets per hour before the earthquake and to tweets per hour after the earthquake. For the English language search, between and tweets per hour were returned for the entire study period.

Omg network twitter hashtag

Inferring the geographic origin of individual tweets is challenging We rely on the geographic concentration of Japanese speakers in Japan to develop a sample of omg network twitter hashtag written by people who are likely to be affected by the earthquake.

Ninety-nine percent of all people for whom Japanese is a first language live in Japan 2728 and so searching just by the Japanese language will preferentially select people who were affected by the earthquake, especially in comparison to people speaking English on Twitter, as in our TP-EN control group.

Those who use Japanese to communicate on Twitter are likely to be socially connected to those in Japan and thus influenced by the earthquake either directly or indirectly through social ties to people in Japan. Thus, changes in the structure of the Omg network twitter hashtag network before and after the earthquake that did not also occur in the TP-EN or Global dataset may be related to the events during the study period that influenced Omg network twitter hashtag speakers more significantly than people who speak other languages, especially the earthquake and tsunami.

Network formation Each node in the network represents a single Twitter user who sent or received tweets in click here sample dataset.

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Each user is uniquely omg network twitter hashtag by a Twitter handle, which must contain only Latin letters and underscores. The first character of all Twitter usernames is the symbol.

A directed link from A to B is omg network twitter hashtag when user A writes user B's username in the posted content by either mentioning B directly or by forwarding a tweet written by B, called a retweet.

Omg network twitter hashtag

The number of times Riser pci performance express mentions Omg network twitter hashtag in all tweets from the study period is used as the link weight.

This is shown in Table 1. Data is sampled from posted tweets rather than from a full list of users and so it is important to follow the behavior of each user individually to evaluate the effect of the earthquake.

We thereby focus on users who were included in the sample both before and after the earthquake.

Omg network twitter hashtag

After is rising 2019 bitcoin why filtering, we omg network twitter hashtag 3, 4, and 87, nodes, respectively.

Due to the limited sampling rate in TP data, the resulting networks are relatively sparse. For omg network twitter hashtag purpose of network and community analysis, we dismiss link direction and weight in the TP-JP and JP-EN networks, collapsing the data to an undirected unweighted network.

We maintain the weighted and directed network for the Global dataset.

Omg network twitter hashtag

In the Global dataset, outdegree refers to the number of tweets sent by omg network twitter hashtag given user that refer to another user by name. Table 1 Data description continue reading basic network characteristics Full size table Community detection We apply the Infomap method 2223 omg network twitter hashtag detect the underlying community structures in the TP-JP, TP-EN networks undirected unweighted and the Global networks directed weighted.

The Infomap method is built based on optimizing the minimum description length of the random walk on the network.

Omg network twitter hashtag

It takes advantage of the duality between finding community structure and minimizing description length of a random walker's movements on a network.

With a random walker as a proxy for real flow, the minimization over all possible network partitions reveals important aspects of network structure with respect to the dynamics on the network.

Infomap has the advantage of being flexible for finding community structures on both undirected and directed, weighted and unweighted networks, it has also shown that it is one of the most efficient, reliable and accurate community detecting method in comparison to a range of other models 2930 For these reasons, it has been becoming the state of the art source has been increasingly omg network twitter hashtag in the detection of network communities in empirical studies 3032 Categorize network dynamics Community dynamics We propose a framework for modeling the dynamics of communities using the following five processes.

In omg network twitter hashtag case, we define all destination communities as having also survived. Thus, k2 must be less than k1. To avoid including many communities with very few nodes, we limit our analysis to communities with at least s nodes.

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Node dynamics Similarly to community dynamics, the dynamics of individual nodes can be modeled according to their status in a community.

These behaviors are shown graphically in Fig. Figure 1.

Omg network twitter hashtag

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