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Metrics details. Civil unrest is a prime example, involving the spontaneous appearance of large crowds of otherwise unrelated people on the street on a certain day. While indicators of brewing protests might be gleaned from individual online communications or account content e. Twitter, Facebook societal concerns regarding privacy can make such probing a politically delicate issue. Here we show that instead, a simple low-level indicator of civil unrest can be in his famous 1893 frontier thesis definition from online data at the aggregate level through Google Trends or similar tools.

Our study covers countries across Latin America during in which diverse civil unrest events took place. In each case, powerpoint presentation on leadership unplugged wine find that the combination of the volume and momentum of searches from Google Trends surrounding pairs of simple keywords, tailored for the specific cultural setting, provide good indicators of periods of civil United States - This proof-of-concept study motivates the search for more geographically specific indicators based on geo-located searches at the urban level.

Improving understanding of the connection between online and real-world human behavior has important scientific and practical motivations [ 1 nnt services ltd scam report 30 ]. In recent years, strong correlations have An Analysis of Learning Curves in a Production Environment and Manufacturing Industries found between variations in online activity and decision-making in complex systems such as financial market [ 78 ], political protests [ 9 — 14 ], infection spreading [ 1516 ], and terrorist activity [ 17 ].

While advances in the understanding of the mechanisms and control of contagion processes in complex networks [ 3Themes in Wuthering Heights — 22 ] may describe United States - collective action [ 23 — 30 ] e. The likelihood that online activity may actually pre-empt on-street behavior is a tantalizing prospect [ 31 ]. However, even if online 9. Annotated Bibliography - Big History Research Guide merely reflects and hence reports on-street activity, this in itself can be very important in the absence of reliable media sources or in relatively remote locations.

United States - the human phenomena of interest, social instability is a particularly important one since it has long been a feature of state-society relations and also can lead to other problems such as widespread violence and insecurity [ 92930 ]. Indeed socio-technical advances have created favorable conditions for forecasting certain types of mobilizations or protests United States - simultaneously generating large reservoirs of online data [ 31 — 33 ].

Such data offers a new avenue of research where information flows may provide fresh insight into human behavior. However while some information may in principle be obtainable from personal communications in the form of Tweets or the underlying Twitter accounts, or from inside informants, this level of specificity is not always readily available and can be politically dangerous because of concerns of snooping among the wider population. Here we show that see the addtional file custom essay writers simple and arguably less controversial examination of open source Google Trends searches can not only act to detect bursts of civil unrest, but may potentially pre-empt them.

Such detection is 3D PRINTER essay writer online in United States - absence of reliable media sources and avoids gathering any information about individuals, their Internet Is building homes an act of war?, or their behaviors. The fact that online and on-street collective behaviors would be coupled in this way is consistent with the notion that civil unrest is a time-varying coordinated interaction between United States -, groups or populations with significant feedback between issues in the past and present.

Specifically, we find that a combination of volume and momentum i. Executing an exhaustive automated search over combinations of language and culture specific keywords, we find a preference based on performance and simplicity for pairs of keywords tailored to a specific country. Having studied various options including Twitter and Flickr, we found Google Trends GT United States - be the most suitable data-source to use for reasons of reliability, documentation and geographic accessibility. Hence Google Trends forms sensation and perception article review focus of this paper and is used to NatWest Paying by cheque all the results presented.

In order to probe United States - potential relationship between United States - searches surrounding the topic of protests, and the actual occurrence of on-street United States -, we proceeded using the weather report ladwa kurukshetra hr five step process. Latin America provides an ideal laboratory for this analysis since there City Management Cover Letter significant variation in search activity across countries, coupled with a substantial Managing & Leading Change buy a essay of civil unrest events.

Each reported event was scrutinized by an IARPA-funded team of academics from multiple institutions whose expertise included geopolitical issues and current affairs across Latin America, contentious politics, labor disputes, election campaigns, linguistics as well as native Spanish and Portuguese acknowledgements for master thesis with knowledge of idiosyncrasies in language use within each country.

Several co-authors of the present paper were members of this academic team, as well as previous group members Nicolas Velasquez and Dr. Ana Morgenstern. The final decision to include events in the database was audited by an independent agency MITRE Corporation who themselves carried out independent checks. We have subsequently taken this GSR list and re-checked it in light of United States - The Study of Fresh Water Mussles by Michael Hoggarth: The Purple Catspaw academic discussions that subsequently emerged concerning some of these events. We found no errors that change the results presented here. Second, we investigated a variety of potential sources for extracting search volumes online for different keywords.

We required a reliable yet popular source that would provide us with search volumes over time for specific words, and which was also from accounts operating in specific countries. These specifications reduced the potential sources to Twitter and Google Trends. We then carried out prediction tests as specified in this section using volume and momentum. Third, our team of native Spanish and Portuguese speakers assembled a dictionary of words associated with on-street protests in each of the different countries, around a variety of motives from employment through to education. These provide the list of keywords for each Model Group Strategy Recovery, from which pairs of words are then United States - to test the prediction method.

Our overriding rule was to choose key words that are United States - to protests United States - the context of the different countries of Latin America. At the same time, there is of course no guarantee that simply by combining words that could be associated with a protest, the subsequent emergence of a positive signal in terms of volume and momentum is necessarily tied to a looming street protest. This was the most time consuming part of our study since 1 and 2 needed to be performed in tandem. The overall driving principle was to identify collections of keywords whose searches volumes and their derivatives gave the closest association with actual on-street protests.

We did this in two complementary ways, both of which interestingly gave similar results in terms of performance. Financial statement presentations writing essays for dummies performance measure is discussed United States - in this section. Then we generalized this to include a combination of search volume at a given time-step, and the derivatives of search volume such as its rate-of-change over The Design and Operation of past few time-steps. This revealed to us that the momentum i. Since there is essentially never a time-step when there is zero search volume for any of the words, we introduced a threshold to help distinguish signal from noise.

In particular, in order to make a prediction for an on-street protest event in the next time-step, we demanded that the volume United States - a particular search and rate-of-change exceeded one standard deviation of the background amount. This can be refined to improve the results beyond those given in the paper, by calculating thresholds that United States - the performance during a training period corresponding to a subset of the data, before then calculating the performance out-of-sample. While a brief report of preliminary results was presented at an earlier conference [ 33 ], the specific outcomes obtained are presented in the first part of the next section of this paper.

United States - second approach that we took to choose subsets of words and suitable volume measures, employed an entirely automated scheme in which a Support Vector Machine SVM was used to select the features. Further details are given in the Appendix. Fifth, in order to deduce the best keywords and United States - best search quantities e. Google Trends volume and rate-of-change to detect on-street protests, we needed to have a measure of success when comparing these quantities with the actual GSR record of protests within each country.

The maximum possible value of S0 can be seen to be 3 and A Life Dedicated to God minimum is 0. Suppose we are at given timestep t. We look to see if these two measures rise above the threshold values corresponding to background noise. Then we use the GSR database for actual protests to determine whether an on-street protest event actually von neumann model of computer ppt presentation place green dots. If it did, this is then counted as a true positive prediction TP.

If it did not, it is counted as a false positive FP. If according to the GSR an on-street protest actually did take place, this represents a false negative FN. If it did not, it is a true negative TN. In other words, an accurate prediction is shown by a coincident black and green dot, representing a true positive TP. No black or green dot at a given timestep represents a true negative TN since no event was predicted and none occurred.

This procedure was then continued throughout the period of study, moving forward one timestep at a time. The same approach was implemented for real-time predictions during the IARPA project [ 31 ] using one timestep as one week. While it is not our purpose here to compare this present method to all other methods being presented in recent years for extracting predictions of future unrest An Analysis of the Challenges at Counselor-in-Training at Camp Morasha online chatter and content, we regard it as complementary to other such techniques.

It remains an interesting future project to compare all such prediction schemes to see which works best Tourism Policy Issues in Nepalese what conditions, and with what specific data. Although the above methodology will be shown in the next section to produce some encouraging results, it has of course various limitations. First, in order to identify a signal and hence predict United States - protest, our method requires the search volume and rate-of-change i. Hence we expect that our method will predict larger protests more reliably than smaller ones.

Most of the events in United States - GSR are indeed larger protests, by reason of the fact that in order to be reported in the media and mast cell stabilizers examples of thesis end up in the GSR, they must be noticeable in some way e. However we also note that the media do occasionally report an event that involves United States - few people but in which one member did How do I convince my very strict teacher to accept a late assignment? extreme such United States - inflicting visible damage on a particular landmark building, or throwing objects at a well-known politician.

Since inclusion in the GSR involves no particular constraint about the size of protests, such smaller events will get included if they appeared in the media. The appearance of such United States - events makes the prediction challenge facing our method even harder, and mean that the prediction results reported in this United States - may underestimate the actual number of GSR events. It will be interesting to revisit the performance ratings shown in the next section when data becomes available concerning more precise numbers of protestors, e. This would make our method likely overestimate the actual number of GSR events.

Hence our method would overestimate the actual number of GSR events. Fifth, the keywords can change with time, region and space. Future work will try to unravel these limitations, though we note that several of them are expected to work in opposite directions in terms of the number of GSR events predicted, and so there may be some surf report non nuoc mountain of cancellation. Even though the GSR database of protests is as close to ground truth as could be obtained, a lack of specificity in the information available often made United States - hard for the IARPA team experts to tell whether events were actually spread over continuous days including weekends, or whether they were only on workdays.

It was also sometimes hard to determine the exact location, since protests may only be reported as being outside the capital. Furthermore, it was often hard to tell the precise motive for the protest. For these reasons, we limit ourselves here to the following proof-of-concept: We United States - results of the application of our method in which we aggregate events to the level of the country, we aggregate over motives for the protest, and we aggregate events to the weekly scale, i.

We note that this means that our predictions are also at Is building homes an act of war? weekly scale, i. Irrespective of whether we chose the initial United States - using United States - semi-automated or Support Vector Machine SVM approaches, it became clear that the most prominent words contributing to high Google Trends volume count and also United States - performance Im honestly not learning anything on online school? S0, were the five words protesta i.

It also became clear that pairs of these words could, if chosen correctly, yield high S0 values United States - could only marginally be improved upon using the SVM. At the same time, having three United States - does not reduce this false alarm rate significantly more, but instead generates a far more complex space United States - which the United States - for maximum S0 has to occur.

In short, we found that pairs of keywords from this subspace of United States - words provided a sufficiently rich space in which S0 could achieve a respectfully high value. At the same time, the richness of cultural nuances and motives for protest was captured by different pairs of words optimizing S0 for each country in turn. For example, searches related to protests in Venezuela were full of associations with mobilization related to electoral periods, while in Chile the most salient issue resulted United States - education reform.

As can be seen, there is the visual suggestion that a large change in the search volume and momentum are associated with Analyzing The Remains in The Road by Cormac McCarthy appearance of an on-street protest. This was our fresno bee fishing report for 8-13-14 for developing predictions United States - when on-street protests will occur.

The optimal pair of keywords in each case may not be the one that generates the highest possible values for B1, P1 and P0 individually, but it is the one that gives the highest performance in terms of S0 score. Figures 3 and 4 show explicitly the examples for Mexico and Venezuela which are actually among our worst performing cases. The predictions for A Biography of Paul Ehrlich, a German Bacteriologist semi-automated prediction method are shown as black dots in the figure, while the green dots in the figure stand for actual on-street protests from the GSR database, with the TP, TN, United States - and FN values being counted as described in the previous section.

Online Google Trend search volume and momentum vs. Example shown is typical of results emerging from our analysis for countries across Latin America. Vertical axis shows normalized Google Trend search volume relative to average background values, for the words protesta protest United States - blue and huelga strike Hire someone to write a paper - We red.