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EliteSingles is meant for a more mature crowd, some of the most powerful people in the world are introverts, or to order multiple courses and pricey drinks. Is the site really completely free? The most simple is the SI sensible-infected model created by Hamer in In this model no individual can be cured.

A harmonic oscillator is an ideal oscillator that evolves over time by a sinusoid, with a frequency independent of the systems properties, and the amplitude is constant.

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Oscillations can be damped, and the equation is hence written as follows:. Epidemiological model displays were done with R with the basic plot function. Experimental implementation of harmonic modelling was done by fast Fourier transform using fft function and least-square in R using function nls stats package [ 61 ]. In this part we try to understand what kind of combinations can be typical of a rumour or a set of messages about a specific rumour.

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We can set two main kinds of combinations. The first ones are lexical n-grams.


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A lexical n-gram is a sequence of n contiguous words separated by a blank. The second kind of combination is a set of 1-gram separated by an n-gram not included in the combination. We tested the capacity of a rumour text to involve a non-standard combination of words. For such studies we used common languages corpora.

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The second experiment is a check of frequent skipgrams consisting of most frequent simple words. In the first experiment we measured originality of a given corpus by the ratio MW c of n-grams not included in a reference corpus by the number of total segments. We used 12 corpus among those four rumours corpus, but also randomly constituted corpora, and corpora based on recent real-world events in French and in English in the present case: Rihanna concert in Europe in summer , and UEFA Euro The measure MW c is expressed as follows:.

Finally, the next step after analyzing lists of features of 2 or 3 words is to measure the incidence of content with vector of words. For that, we cannot use the DIS-corpus because each rumour is unique and a set of ten or twenty words could not show similarity with other rumours. But if we take the Twitter rumours, we can observe how people talk about a rumour and compare the specificity of rumour discourse with ordinary messages.

We would like now get an overview of words importance in the rumorous content over time. Recall that Allport, and Postman, 51 specifies a rumor mechanisms in three different mechanisms applicable in any situation. The first mechanism is a selection of main features leveling, or loss of details. The second mechanism is sharpening refers to is an emphasis of some details during the transmission.

Finally the last mechanism, assimilation refers to a distortion in the transmission of information. Linguistic assimilation usually consisted of inserting the words "is," "is as," "as," or "it's" or noise. Let suppose a rumor starts with nine details and ends with three, they would say that six were leveled and three were sharpened. Another angle to capture association is machine learning algorithms. Why, because machine learning algorithms use features, often within non-linear techniques indirectly taking into account combination of features.

In summary, it captures correlation of features to make a good prediction without specifying association between features. The first question that arises, due to sensitivity of algorithms to the feature space, is to define the dimensionality of the feature space. We can take the whole set of words between 3, and 4, words but it can be time consuming for some techniques or noise generation.

We make a documents x terms matrix using different samples, i. We consider that rumorous messages starting by the same 70 characters half of the message are the same and we delete them for building the dataset. Hence the dataset consists of 1, messages containing all the four rumors messages, the pool of message to predict.

We mixed this subset with 9, non-rumor messages. As training dataset we chose all the rumor subset and 2, non-rumor messages. As baseline for comparison of techniques we consider the random assignment. A message can be assigned randomly as rumorous or non-rumorous. Hence for each classification method we compute two indicators that are the global accuracy that we want enough high better than random for a stream of both rumorous and non-rumorous messages, and accuracy specific for rumorous messages that we expect also close to random score.

In the next experiment we keep the same matrix as before with most frequent feature space but we change the document space.

Amount of non-rumorous messages in test set is always about 8, messages, and for the train set we keep the same amount than the rumorous set about or 2, messages. The experimental implementation was done in R.

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The syntagmatic extraction is a function using regular expression analysis with gsub function base package , multi-word extraction with ngram function ngram package , and data cleaning using a stopwords list. Fig 3 displays time distribution of tweets emission by users for each rumour. We can see that no plot really can fit with a 2-local maximum distribution, as shown on Fig 2.

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An advantage of the oscillator model is that it produces several local maxima see Fig 5 , whereas epidemiological models produce only one or two local maxima. Fig 4 shows us a fit of Hidalgo-corpus with a damped oscillator model.

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It fits quite well, and better than any epidemiological model. But it seems that amplitude is not stable. Table 4 shows us a list of frequent n-grams for each corpus of rumours: Hidalgo-corpus, Lemon-corpus, Pin-corpus and swine-corpus. We cleaned n-grams by subtracting the prefix or suffix matching with stopwords. Processing is done in both languages. In Table 4 no information appears to make sense for a rumour in general. Next we tried to extract the most frequent simple words over the 1, rumourous texts 1, in English, in French. Table 5 shows the most frequent words in the database by decreasing order of occurrences or documents.

Among these words we can see only two words about a specific topic cancer, Obama and no word very typical for a rumourous alert. If we consider the number of documents, words are relevant 64 in French, 96 in English. Most of words are very short two or three characters. Many words seem to be tool words such as: pro , ex , hey , side , app , etc. Another big cluster of words are general verbs such as go , use , eat , see , etc. Some general meaning words seems recurrent too such as men , one , day , king , war , ease , etc. We cannot extract any global argumentative structure of a rumour that is redundant across a large set of documents.

Table 6 represents another view of word frequency in the text database. It points out the distribution of lexical units 1-grams over each database French, English. We can also find prepositions or adverbs such as like , now , us.