This is a table of type trigram and their frequencies. Use it to search & browse the list to learn more about your study carrel.
trigram | frequency |
---|---|
the number of | 269 |
as well as | 144 |
in order to | 129 |
on social media | 122 |
of social media | 117 |
one of the | 105 |
based on the | 105 |
in terms of | 89 |
the united states | 87 |
number of tweets | 84 |
more likely to | 82 |
due to the | 81 |
in social media | 79 |
the use of | 78 |
the spread of | 69 |
a number of | 66 |
in this study | 62 |
during the covid | 61 |
in the united | 58 |
of altmetric data | 56 |
of the tweets | 56 |
the impact of | 54 |
of the pandemic | 53 |
likelihood of sharing | 53 |
such as the | 52 |
analysis of the | 51 |
likely to be | 51 |
social media data | 50 |
the context of | 50 |
the case of | 48 |
social media platforms | 47 |
hot research topics | 47 |
social media and | 46 |
a set of | 45 |
to understand the | 45 |
of the most | 44 |
the fact that | 44 |
higher levels of | 43 |
the presence of | 43 |
total number of | 42 |
as a result | 42 |
can be used | 41 |
there is a | 41 |
in this paper | 41 |
of the covid | 39 |
natural language processing | 39 |
related to the | 39 |
the social media | 39 |
according to the | 38 |
as shown in | 38 |
be used to | 37 |
most of the | 36 |
in the context | 35 |
the relationship between | 34 |
the majority of | 34 |
well as the | 34 |
local tv viewership | 34 |
each of the | 34 |
at the time | 34 |
the distribution of | 33 |
of this study | 33 |
on the other | 33 |
of the virus | 33 |
the time of | 32 |
in addition to | 32 |
a list of | 32 |
we used the | 32 |
the total number | 31 |
an analysis of | 31 |
in the same | 31 |
the role of | 31 |
the results of | 30 |
during the pandemic | 30 |
it is possible | 30 |
used in the | 29 |
is based on | 29 |
in this section | 29 |
the effect of | 29 |
in the future | 29 |
shown in fig | 29 |
world health organization | 28 |
we found that | 28 |
to display the | 28 |
the importance of | 28 |
a total of | 28 |
associated with the | 28 |
of the tweet | 28 |
in the first | 28 |
stages of the | 28 |
in the uk | 28 |
and social media | 28 |
granted medrxiv a | 27 |
who has granted | 27 |
the percentage of | 27 |
the age of | 27 |
has granted medrxiv | 27 |
medrxiv a license | 27 |
the other hand | 27 |
some of the | 27 |
of tweets in | 27 |
license to display | 27 |
the field of | 27 |
of machine learning | 27 |
preprint in perpetuity | 27 |
use of twitter | 27 |
a license to | 27 |
the preprint in | 27 |
display the preprint | 27 |
of the outbreak | 27 |
all of the | 26 |
to analyze the | 26 |
international license it | 25 |
available under a | 25 |
the frequency of | 25 |
of twitter users | 25 |
to identify the | 25 |
it is made | 25 |
in this case | 25 |
made available under | 25 |
the analysis of | 25 |
the copyright holder | 25 |
is made available | 25 |
and machine learning | 25 |
analysis of tweets | 25 |
license it is | 25 |
analysis of twitter | 25 |
social big data | 24 |
are more likely | 24 |
holder for this | 24 |
it is a | 24 |
of twitter followers | 24 |
were more likely | 24 |
spread of the | 24 |
such as twitter | 24 |
the rest of | 24 |
table shows the | 24 |
use of social | 24 |
the performance of | 24 |
for this preprint | 24 |
social media users | 24 |
of hate speech | 24 |
copyright holder for | 24 |
in the field | 23 |
in which the | 23 |
social media use | 23 |
is the author | 23 |
team financial performance | 23 |
state of alarm | 23 |
to be a | 23 |
the author funder | 23 |
of the data | 23 |
shown in table | 23 |
social media to | 23 |
the end of | 23 |
there is no | 23 |
in the case | 23 |
the beginning of | 22 |
was used to | 22 |
from social media | 22 |
tweets in the | 22 |
and the number | 22 |
the purpose of | 22 |
of brand equity | 22 |
found that the | 22 |
the same time | 21 |
in the age | 21 |
it is also | 21 |
content analysis of | 21 |
of the public | 21 |
the set of | 21 |
because of the | 21 |
it can be | 21 |
were used to | 21 |
use of the | 21 |
under a is | 21 |
words in the | 21 |
of tweets and | 21 |
a is the | 21 |
the proportion of | 21 |
compared to the | 21 |
are shown in | 21 |
shown in figure | 21 |
to the covid | 21 |
figure shows the | 21 |
this preprint this | 20 |
are related to | 20 |
the probability of | 20 |
in the data | 20 |
the content of | 20 |
social media is | 20 |
preprint this version | 20 |
using social media | 20 |
this version posted | 20 |
and local tv | 20 |
of the crisis | 20 |
around the world | 20 |
the size of | 20 |
of the number | 20 |
the amount of | 20 |
of the two | 20 |
a case study | 20 |
major league baseball | 19 |
to the pandemic | 19 |
the h n | 19 |
a variety of | 19 |
the most popular | 19 |
in the following | 19 |
rest of the | 19 |
use social media | 19 |
for public health | 19 |
of the network | 19 |
is consistent with | 19 |
of twitter data | 19 |
this is the | 19 |
we observe that | 19 |
international conference on | 19 |
can be seen | 19 |
in this work | 19 |
it is worth | 19 |
the most common | 19 |
of the users | 19 |
number of retweets | 19 |
we see that | 18 |
in the social | 18 |
are likely to | 18 |
of all the | 18 |
is important to | 18 |
it is not | 18 |
related to covid | 18 |
of the three | 18 |
of tweets that | 18 |
part of the | 18 |
can also be | 18 |
in the number | 18 |
at least one | 18 |
to the public | 18 |
x x x | 18 |
latent dirichlet allocation | 18 |
is one of | 18 |
measure of concern | 18 |
smt immunity link | 18 |
number of publications | 18 |
based on a | 18 |
social network analysis | 18 |
a result of | 18 |
the need for | 18 |
distribution of the | 18 |
response to the | 18 |
with the same | 18 |
relationship between non | 17 |
a smt immunity | 17 |
to predict the | 17 |
social media in | 17 |
included in the | 17 |
the effectiveness of | 17 |
using twitter data | 17 |
it has been | 17 |
the tweets in | 17 |
the behavior of | 17 |
in response to | 17 |
we find that | 17 |
focus on the | 17 |
it would be | 17 |
of verified users | 17 |
in the us | 17 |
the degree of | 17 |
to focus on | 17 |
in support of | 17 |
a sample of | 17 |
in the past | 17 |
that have been | 17 |
and so on | 17 |
it is important | 17 |
is the most | 16 |
number of users | 16 |
large number of | 16 |
is that the | 16 |
in real time | 16 |
which is the | 16 |
of sharing the | 16 |
is used to | 16 |
that can be | 16 |
a combination of | 16 |
words related to | 16 |
the problem of | 16 |
that there is | 16 |
high level of | 16 |
of the social | 16 |
the level of | 16 |
to detect the | 16 |
the coverage of | 16 |
words such as | 16 |
shows that the | 16 |
tweets related to | 16 |
can be found | 16 |
for each user | 16 |
the twitter api | 16 |
an overview of | 16 |
of the main | 16 |
is associated with | 16 |
in the tweets | 16 |
the most important | 16 |
the public health | 16 |
with respect to | 16 |
members of the | 16 |
are able to | 16 |
in the present | 16 |
appears to be | 16 |
early stages of | 15 |
is possible that | 15 |
the usage of | 15 |
in relation to | 15 |
it should be | 15 |
impact of covid | 15 |
at the same | 15 |
for social media | 15 |
high levels of | 15 |
of words in | 15 |
for each of | 15 |
on twitter during | 15 |
a social media | 15 |
focused on the | 15 |
to do this | 15 |
perception of the | 15 |
social media for | 15 |
with regard to | 15 |
terms of the | 15 |
levels of abuse | 15 |
the measure of | 15 |
new media literacy | 15 |
are used to | 15 |
number of replies | 15 |
what are the | 15 |
of tweets during | 15 |
it is the | 15 |
time of the | 15 |
the evolution of | 15 |
which is a | 15 |
the name of | 15 |
the effects of | 15 |
the machine learning | 15 |
coverage and intensity | 15 |
that it is | 15 |
are based on | 15 |
is an important | 15 |
majority of the | 15 |
in the form | 15 |
used as a | 15 |
number of times | 15 |
that machine learning | 15 |
in times of | 15 |
the form of | 15 |
likely to share | 14 |
in the network | 14 |
version posted may | 14 |
a subset of | 14 |
democrat and republican | 14 |
case of the | 14 |
a comparison of | 14 |
number of posts | 14 |
resilient communication ecosystem | 14 |
derived from the | 14 |
machine learning algorithms | 14 |
twitter as a | 14 |
found in the | 14 |
increase in the | 14 |
the early stages | 14 |
nature of the | 14 |
we use the | 14 |
is possible to | 14 |
the likelihood of | 14 |
and it is | 14 |
used in this | 14 |
theory of brand | 14 |
different types of | 14 |
tweets that are | 14 |
to be the | 14 |
this study is | 14 |
may not be | 14 |
in the usa | 14 |
a series of | 14 |
the extent to | 14 |
unified theory of | 14 |
extent to which | 14 |
of the news | 14 |
the world health | 14 |
the correlation between | 14 |
for the period | 14 |
this is a | 14 |
is shown in | 14 |
support of h | 14 |
tweets that were | 14 |
from march to | 14 |
tweets during the | 14 |
in social networks | 14 |
in other words | 14 |
to capture the | 14 |
associated with a | 14 |
needs to be | 14 |
an example of | 14 |
gender differences in | 14 |
of fake news | 14 |
the data collection | 14 |
of all abuse | 14 |
the concept of | 14 |
personal versus news | 14 |
the new york | 14 |
during the first | 14 |
of social distancing | 14 |
the basis of | 14 |
users in the | 14 |
understanding of the | 14 |
this means that | 13 |
social distancing and | 13 |
many of the | 13 |
from the twitter | 13 |
in the twitter | 13 |
depending on the | 13 |
the novel coronavirus | 13 |
the rise of | 13 |
the validated network | 13 |
we use a | 13 |
are associated with | 13 |
the peaks of | 13 |
semantic network analysis | 13 |
consistent with the | 13 |
based on their | 13 |
using the twitter | 13 |
and number of | 13 |
is defined as | 13 |
season marketing assets | 13 |
a personal tweet | 13 |
lower levels of | 13 |
from january to | 13 |
social media as | 13 |
tweets and the | 13 |
relationship between in | 13 |
how social media | 13 |
on twitter and | 13 |
reported likelihood of | 13 |
the goal of | 13 |
of the information | 13 |
for each tweet | 13 |
the state of | 13 |
the tweet is | 13 |
we propose a | 13 |
in the early | 13 |
levels of twitter | 13 |
of the word | 13 |
for all the | 13 |
likely to have | 13 |
the influence of | 13 |
for this reason | 13 |
the definition of | 13 |
number of topics | 13 |
in the previous | 13 |
the label propagation | 13 |
described in section | 13 |
in the world | 13 |
were associated with | 13 |
the potential to | 13 |
the volume of | 13 |
different stages of | 13 |
in this context | 13 |
the maintenance stage | 13 |
to extract the | 13 |
size of the | 13 |
with higher levels | 13 |
marketing assets and | 13 |
in the analysis | 13 |
during the early | 13 |
the list of | 13 |
personal negative tweets | 13 |
in our dataset | 13 |
as opposed to | 13 |
of the total | 13 |
of the first | 13 |
h n outbreak | 13 |
and found that | 13 |
negative versus non | 12 |
a high level | 12 |
on the basis | 12 |
of public health | 12 |
times of crisis | 12 |
the measles outbreak | 12 |
the higher the | 12 |
network of verified | 12 |
results of the | 12 |
online social networks | 12 |
would like to | 12 |
supported by the | 12 |
we want to | 12 |
of the paper | 12 |
be able to | 12 |
it does not | 12 |
publications in the | 12 |
state of the | 12 |
seasonal marketing assets | 12 |
the geocov tweets | 12 |
other social media | 12 |
was associated with | 12 |
the dynamics of | 12 |
democrats and republicans | 12 |
be seen in | 12 |
right and right | 12 |
in our case | 12 |
as it is | 12 |
to determine the | 12 |
the tweets were | 12 |
information about the | 12 |
be used as | 12 |
been shown to | 12 |
can be applied | 12 |
each hashtag group | 12 |
and right wing | 12 |
number of followers | 12 |
state sponsored media | 12 |
performance of the | 12 |
of the political | 12 |
a review of | 12 |
refers to the | 12 |
geocov tweets dataset | 12 |
during this time | 12 |
in the literature | 12 |
all the tweets | 12 |
in patients with | 12 |
the frames used | 12 |
in the dataset | 12 |
attendance and local | 12 |
a measure of | 12 |
the tweets and | 12 |
a lot of | 12 |
is able to | 12 |
is necessary to | 12 |
features of the | 12 |
with the help | 12 |
the formation of | 12 |
found to be | 12 |
represented by the | 12 |
defined as the | 12 |
positive and negative | 12 |
the nature of | 12 |
the perception of | 12 |
during the h | 12 |
machine learning systems | 12 |
appear to be | 12 |
with a high | 12 |
the value of | 12 |
on social networks | 12 |
the start of | 12 |
online social media | 12 |
the help of | 12 |
topics in the | 12 |
public health frame | 12 |
it is necessary | 11 |
during this period | 11 |
media use in | 11 |
in this chapter | 11 |
need to be | 11 |
we created a | 11 |
on the same | 11 |
characteristics of the | 11 |
twitter has been | 11 |
with the highest | 11 |
more than one | 11 |
to the fact | 11 |
the existence of | 11 |
we used a | 11 |
the pandemic and | 11 |
the appearance of | 11 |
seen in figure | 11 |
in the time | 11 |
social media during | 11 |
the possibility of | 11 |
most altmetric data | 11 |
of the current | 11 |
context of the | 11 |
presence of altmetric | 11 |
be noted that | 11 |
of the disease | 11 |
network analysis of | 11 |
a large number | 11 |
a tweet is | 11 |
which can be | 11 |
the university of | 11 |
highest number of | 11 |
per number of | 11 |
in the digital | 11 |
new york times | 11 |
changes in the | 11 |
we can find | 11 |
of the study | 11 |
that social media | 11 |
sentiment analysis and | 11 |
versus news classification | 11 |
text of the | 11 |
online hate speech | 11 |
media coverage of | 11 |
diffusion patterns of | 11 |
there is an | 11 |
out of the | 11 |
coverage of the | 11 |
number of population | 11 |
as one of | 11 |
diversion and suppression | 11 |
the general public | 11 |
relationship between the | 11 |
official accounts of | 11 |
to measure the | 11 |
to account for | 11 |
levels of attention | 11 |
and in the | 11 |
be found in | 11 |
the pandemic in | 11 |
deaths per number | 11 |
of conspiracy narratives | 11 |
in regard to | 11 |
the lack of | 11 |
refer to the | 11 |
resulting in a | 11 |
across subject fields | 11 |
the topic of | 11 |
the absence of | 11 |
each of these | 11 |
two types of | 11 |
platforms such as | 11 |
a function of | 11 |
in this article | 11 |
to examine the | 11 |
social networking sites | 11 |
in the appendix | 11 |
as the pandemic | 11 |
types of altmetric | 11 |
and team financial | 11 |
the twitter data | 11 |
social media has | 11 |
machine learning is | 11 |
frustration and hope | 11 |
more than million | 11 |
of the population | 11 |
the date of | 11 |
to understand how | 11 |
this kind of | 11 |
a health crisis | 11 |
for the covid | 11 |
of the world | 11 |
the types of | 11 |
the highest number | 11 |
showed that the | 11 |
words associated with | 11 |
results show that | 11 |
this data is | 11 |
representation of the | 11 |
higher than the | 11 |
age of twitter | 11 |
the first step | 11 |
along with the | 11 |
purpose of this | 11 |
is likely to | 10 |
this research is | 10 |
labeled as a | 10 |
to study the | 10 |
hate speech detection | 10 |
can be a | 10 |
the structure of | 10 |
the most frequently | 10 |
by twitter users | 10 |
machine learning and | 10 |
allows us to | 10 |
may lead to | 10 |
to have a | 10 |
that were not | 10 |
through social media | 10 |
have been used | 10 |
the tweets that | 10 |
related to a | 10 |
is the case | 10 |
of abusive replies | 10 |
contribute to the | 10 |
the network of | 10 |
a survey of | 10 |
as a tool | 10 |
that epic m | 10 |
in the last | 10 |
to find the | 10 |
known to be | 10 |
we need to | 10 |
social media platform | 10 |
defined as a | 10 |
of this research | 10 |
validated network of | 10 |
a resilient communication | 10 |
digital media literacy | 10 |
that were used | 10 |
pandemics in the | 10 |
set of tweets | 10 |
result of the | 10 |
facebook and instagram | 10 |
different from the | 10 |
of all tweets | 10 |
caused by the | 10 |
of the twitter | 10 |
the social network | 10 |
daily number of | 10 |
as discussed in | 10 |
be more likely | 10 |
we also observe | 10 |
in a tweet | 10 |
the tweet text | 10 |
in the tweet | 10 |
were able to | 10 |
using the same | 10 |
web of science | 10 |
can be defined | 10 |
be applied to | 10 |
we can see | 10 |
there are two | 10 |
in the use | 10 |
that most of | 10 |
hair et al | 10 |
of the model | 10 |
and can be | 10 |
and public health | 10 |
be defined as | 10 |
in recent years | 10 |
in a given | 10 |
between the two | 10 |
was based on | 10 |
as a function | 10 |
in the supplementary | 10 |
to reduce the | 10 |
the unified theory | 10 |
the application of | 10 |
the world and | 10 |
public health professional | 10 |
such as facebook | 10 |
political orientation of | 10 |
for the purpose | 10 |
during a crisis | 10 |
via social media | 10 |
fuzzy accuracy assessment | 10 |
can lead to | 10 |
is not a | 10 |
as the number | 10 |
role in the | 10 |
language processing and | 10 |
tweets containing the | 10 |
version of the | 10 |
to the best | 10 |
that they had | 10 |
frames used in | 10 |
social media can | 10 |
based on twitter | 10 |
we present the | 10 |
a social network | 10 |
to explore the | 10 |
to deal with | 10 |
the fields of | 10 |
in the u | 10 |
level of attention | 10 |
to the data | 10 |
the following tweet | 10 |
rather than a | 10 |
of social networks | 10 |
should be noted | 10 |
in online social | 10 |
the recovery period | 10 |
altmetric data for | 10 |
portion of the | 10 |
content of the | 10 |
support vector machine | 10 |
a news tweet | 10 |
an increase in | 10 |
case study of | 10 |
that the model | 10 |
the data from | 10 |
the part of | 10 |
a semantic network | 9 |
for disease control | 9 |
in line with | 9 |
to social media | 9 |
of a tweet | 9 |
and game attendance | 9 |
such as a | 9 |
social and machine | 9 |
research topics are | 9 |
it may be | 9 |
policy document citations | 9 |
description of the | 9 |
proceedings of the | 9 |
while it is | 9 |
the middle of | 9 |
i is the | 9 |
data presence across | 9 |
the collected data | 9 |
are the most | 9 |
was used as | 9 |
us presidential election | 9 |
the diffusion patterns | 9 |
that we are | 9 |
support for the | 9 |
in our sample | 9 |
the first two | 9 |
to provide a | 9 |
machine learning methods | 9 |
the vast majority | 9 |
summarised in table | 9 |
level of abuse | 9 |
was used in | 9 |
higher likelihood of | 9 |
on the topic | 9 |
by the public | 9 |
the words that | 9 |
news update frame | 9 |
followed by the | 9 |
the collection of | 9 |
extracted from the | 9 |
at this time | 9 |
that our model | 9 |
the democrat and | 9 |
cov tweets dataset | 9 |
to social distancing | 9 |
were likely to | 9 |
we set the | 9 |
as a personal | 9 |
the emergence of | 9 |
seen them before | 9 |
in conjunction with | 9 |
this type of | 9 |
for this purpose | 9 |
research topics in | 9 |
we are able | 9 |
in the current | 9 |
event detection from | 9 |
to changes in | 9 |
the best of | 9 |
vast majority of | 9 |
a public health | 9 |
the vaccine frame | 9 |
of the topics | 9 |
relevant to the | 9 |
for event detection | 9 |
on twitter data | 9 |
relative to the | 9 |
the focus of | 9 |
to be retweeted | 9 |
that there are | 9 |
for each word | 9 |
has been used | 9 |
as a percentage | 9 |
of uk mps | 9 |
number of covid | 9 |
the path between | 9 |
for sentiment analysis | 9 |
of the existing | 9 |
the political frame | 9 |
which was not | 9 |
marketing assets are | 9 |
to investigate the | 9 |
abuse sent to | 9 |
twitter data to | 9 |
to the number | 9 |
on a large | 9 |
positively related to | 9 |
the result of | 9 |
is labeled as | 9 |
percentage of the | 9 |
this is consistent | 9 |
review of the | 9 |
words that are | 9 |
number of cases | 9 |
note that the | 9 |
tweets posted by | 9 |
of social and | 9 |
information of the | 9 |
fact that the | 9 |
to work with | 9 |
in the next | 9 |
the notion of | 9 |
we did not | 9 |
it is likely | 9 |
shown in the | 9 |
worth noting that | 9 |
is represented by | 9 |
shows the results | 9 |
all of these | 9 |
the development of | 9 |
media and the | 9 |
related to social | 9 |
the findings of | 9 |
cases and deaths | 9 |
the twitter users | 9 |
contribution to the | 9 |
structure of the | 9 |
has also been | 9 |
similar to the | 9 |
the onset of | 9 |
perception of risk | 9 |
number of deaths | 9 |
have shown that | 9 |
topics that are | 9 |
of the user | 9 |
twitter rest api | 9 |
sales and marketing | 9 |
the popularity of | 9 |
in the second | 9 |
order to identify | 9 |
sent to mps | 9 |
the period of | 9 |
with the number | 9 |
is designed to | 9 |
overview of the | 9 |
at the university | 9 |
on the relationship | 9 |
a sentiment analysis | 9 |
on twitter in | 9 |
between in season | 9 |
the word co | 9 |
on the internet | 9 |
the potential of | 9 |
the daily number | 9 |
women of colour | 9 |
the word cloud | 9 |
in figure we | 9 |
and social distancing | 9 |
the scope of | 9 |
more than of | 9 |
table presents the | 9 |
evolution of the | 9 |
abuse for the | 9 |
the pandemic has | 9 |
the political orientation | 9 |
seasonal ma and | 9 |
of data collection | 9 |
as hot research | 9 |
of this paper | 9 |
of coverage and | 9 |
data from social | 9 |
of abuse for | 9 |
right wing parties | 9 |
tend to be | 9 |
by the number | 9 |
by the authors | 9 |
twitter during the | 9 |
for this study | 9 |
all abuse sent | 9 |
on boris johnson | 9 |
is worth noting | 9 |
context of covid | 9 |
can be represented | 9 |
social media messages | 8 |
the sir model | 8 |
sample of tweets | 8 |
allows users to | 8 |
we consider the | 8 |
number of mentions | 8 |
of likelihood of | 8 |
on the data | 8 |
altmetric data with | 8 |
while there are | 8 |
social media activities | 8 |
the idea that | 8 |
we aim to | 8 |
do not have | 8 |
the official accounts | 8 |
that twitter users | 8 |
the time series | 8 |
present in the | 8 |
and team financials | 8 |
are presented in | 8 |
greater likelihood of | 8 |
of million tweets | 8 |
parts of the | 8 |
allow users to | 8 |
o o f | 8 |
detecting influenza epidemics | 8 |
the actions of | 8 |
the tweet was | 8 |
the results are | 8 |
on the part | 8 |
tweets in our | 8 |
in the death | 8 |
peer review comments | 8 |
the three disinformation | 8 |
and el mundo | 8 |
tweets that contain | 8 |
conflict of interest | 8 |
the public to | 8 |
the mention network | 8 |
of this article | 8 |
the risk of | 8 |
of the topic | 8 |
the supplementary information | 8 |
disease control and | 8 |
information on social | 8 |
big data and | 8 |
data from twitter | 8 |
of the new | 8 |
if it is | 8 |
to machine learning | 8 |
the action words | 8 |
was significantly predicted | 8 |
p r o | 8 |
activity of the | 8 |
symptoms of covid | 8 |
there are several | 8 |
in the study | 8 |
in each country | 8 |
less likely to | 8 |
goal of this | 8 |
l p r | 8 |
to compare the | 8 |
into account the | 8 |
but also to | 8 |
to use the | 8 |
the average of | 8 |
data collection and | 8 |
discussion of the | 8 |
show that the | 8 |
the analysis was | 8 |
most prominent news | 8 |
to calculate the | 8 |
to note that | 8 |
corresponding to the | 8 |
the sentiment analysis | 8 |
addition to the | 8 |
be useful to | 8 |
are interested in | 8 |
on the social | 8 |
the us presidential | 8 |
the potential reach | 8 |
in studies and | 8 |
to obtain the | 8 |
the news frames | 8 |
the power of | 8 |
the last years | 8 |
identified in the | 8 |
the quality of | 8 |
j o u | 8 |
data mining techniques | 8 |
see appendix a | 8 |
the covid crisis | 8 |
of replies that | 8 |
if the tweet | 8 |
across social media | 8 |
whether or not | 8 |
use of hashtags | 8 |
data from the | 8 |
to be true | 8 |
in the covid | 8 |
compared to other | 8 |
be explained by | 8 |
personal protective equipment | 8 |
the authors declare | 8 |
of twitter use | 8 |
be due to | 8 |
goal is to | 8 |
we are interested | 8 |
to the president | 8 |
were asked to | 8 |
significantly predicted by | 8 |
a l p | 8 |
positive or negative | 8 |
united states during | 8 |
tweets can be | 8 |
is that it | 8 |
the most frequent | 8 |
during the period | 8 |
the initial stage | 8 |
in each tweet | 8 |
a tool for | 8 |
deaths per one | 8 |
specific altmetric data | 8 |
to ensure that | 8 |
in this category | 8 |
we see a | 8 |
shared untrue material | 8 |
in the corpus | 8 |
figure illustrates the | 8 |
to mps in | 8 |
cascades on twitter | 8 |
there are also | 8 |
static and dynamic | 8 |
to get the | 8 |
the united kingdom | 8 |
the expanded analysis | 8 |
altmetric data presence | 8 |
the steel blue | 8 |
the text of | 8 |
the characteristics of | 8 |
topics of interest | 8 |
at the end | 8 |
of the collected | 8 |
mlb brand equity | 8 |
peaks of news | 8 |
u r n | 8 |
and the other | 8 |
tweets and retweets | 8 |
that do not | 8 |
across the world | 8 |
the data is | 8 |
source of information | 8 |
to some extent | 8 |
to the right | 8 |
in our model | 8 |
united states and | 8 |
is presented in | 8 |
words in each | 8 |
of psychophysical numbing | 8 |
as in study | 8 |
it difficult to | 8 |
the same way | 8 |
and data analysis | 8 |
and the public | 8 |
as part of | 8 |
a systematic review | 8 |
we were able | 8 |
gorrell et al | 8 |
of our knowledge | 8 |
observe that the | 8 |
the identification of | 8 |
this is not | 8 |
means that the | 8 |
in which they | 8 |
the fraction of | 8 |
of the analysis | 8 |
on the number | 8 |
to create a | 8 |
the european union | 8 |
altmetric data sources | 8 |
a greater likelihood | 8 |
user u i | 8 |
the accuracy of | 8 |
social media sites | 8 |
on the analysis | 8 |
the activity of | 8 |
and there are | 8 |
pandemic in the | 8 |
is required to | 8 |
about the virus | 8 |
in our analysis | 8 |
p r e | 8 |
this paper is | 8 |
to make the | 8 |
on the web | 8 |
a study of | 8 |
properties of the | 8 |
to monitor the | 8 |
the supplementary material | 8 |
development of a | 8 |
the confusion matrix | 8 |
of alarm and | 8 |
r n a | 8 |
using twitter to | 8 |
used to detect | 8 |
chen et al | 8 |
a sense of | 8 |
in the center | 8 |
phrase i in | 8 |
r o o | 8 |
the accounts of | 8 |
the data and | 8 |
the authors also | 8 |
they had seen | 8 |
of attention on | 8 |
days of the | 8 |
users who reported | 8 |
aspects of the | 8 |
also be used | 8 |
is also a | 8 |
with each other | 8 |
we plan to | 8 |
are consistent with | 8 |
the kinds of | 8 |
has led to | 8 |
correlation between the | 8 |
by the user | 8 |
n a l | 8 |
is an open | 8 |
media such as | 8 |
data in the | 8 |
number of confirmed | 8 |
to estimate the | 8 |
public response to | 8 |
there was a | 8 |
twitter data for | 8 |
social media are | 8 |
of users and | 8 |
and spread of | 8 |
with at least | 8 |
and how they | 8 |
been applied to | 8 |
directed validated network | 8 |
the raw data | 8 |
have the potential | 8 |
the process of | 8 |
and domestic abuse | 8 |
indicated that the | 8 |
the ability to | 8 |
o u r | 8 |
that of the | 8 |
some of these | 8 |
impact of the | 7 |
the need to | 7 |
that the public | 7 |
how the public | 7 |
beginning of the | 7 |
a percentage of | 7 |
words in a | 7 |
political frame was | 7 |
is provided in | 7 |
the frequency with | 7 |
take into account | 7 |
the burden on | 7 |
used to identify | 7 |
the second step | 7 |
is the number | 7 |
or of the | 7 |
the stories were | 7 |
topics related to | 7 |
sciences and humanities | 7 |
that are related | 7 |
to try to | 7 |
twitter streaming api | 7 |
a crucial role | 7 |
as measured by | 7 |
assets and team | 7 |
may be more | 7 |
and th inclusive | 7 |
by using the | 7 |
were used for | 7 |
of the problem | 7 |
orientation of users | 7 |
is the first | 7 |
that are currently | 7 |
q a mentions | 7 |
increasing number of | 7 |
are provided in | 7 |
tweets concerned with | 7 |
between non seasonal | 7 |
inverse document frequency | 7 |
the same as | 7 |
the bipartite network | 7 |
they may be | 7 |
april st and | 7 |
non seasonal and | 7 |
research topics with | 7 |
data for the | 7 |
on day t | 7 |
social sciences and | 7 |
to jurisdictional claims | 7 |
any of the | 7 |
the time period | 7 |
account for the | 7 |
to better understand | 7 |
twitter sentiment analysis | 7 |
the first three | 7 |
mps between april | 7 |
to include the | 7 |
was found to | 7 |
during times of | 7 |
and risk communication | 7 |
be related to | 7 |
the positive relationship | 7 |
and after the | 7 |
to get a | 7 |
there have been | 7 |
for this task | 7 |
note springer nature | 7 |
march to april | 7 |
of data mining | 7 |
the dependent variable | 7 |
of the event | 7 |
the directed validated | 7 |
we will use | 7 |
percentage of replies | 7 |
historical sharing of | 7 |
terms such as | 7 |
context in which | 7 |
information cascades on | 7 |
social distancing rules | 7 |
social media mining | 7 |
pew research center | 7 |
nodes in the | 7 |
is a significant | 7 |
regard to jurisdictional | 7 |
to mps between | 7 |
public perception of | 7 |
to represent the | 7 |
tweets collected between | 7 |
claims in published | 7 |
and likelihood of | 7 |
for the first | 7 |
as a predictor | 7 |
associated with higher | 7 |
to reflect the | 7 |
so as to | 7 |
in season and | 7 |
on facebook and | 7 |
in each hashtag | 7 |
mcrobbie and thornton | 7 |
a lack of | 7 |
tweets in each | 7 |
topic modeling and | 7 |
accounts in the | 7 |
in more detail | 7 |
the top ten | 7 |
the data for | 7 |
users on twitter | 7 |
in the validated | 7 |
to obtain a | 7 |
is organized as | 7 |
potential reach of | 7 |
that the results | 7 |
to each other | 7 |
terms related to | 7 |
at the micro | 7 |
to see if | 7 |
there is evidence | 7 |
machine learning framework | 7 |
fake news detection | 7 |
the complexity of | 7 |
amount of data | 7 |
the data were | 7 |
of the art | 7 |
we will discuss | 7 |
is related to | 7 |
i in t | 7 |
the study was | 7 |
the one of | 7 |
frequency with which | 7 |
the seriousness of | 7 |
the work of | 7 |
the increase in | 7 |
to increase the | 7 |
subject fields and | 7 |
positive relationship between | 7 |
to visualize the | 7 |
jurisdictional claims in | 7 |
in case of | 7 |
by public health | 7 |
number of the | 7 |
severe acute respiratory | 7 |
on how to | 7 |
exploratory analysis of | 7 |
correlated with the | 7 |
to a specific | 7 |
shown to be | 7 |
look at the | 7 |
name of the | 7 |
to support the | 7 |
of the models | 7 |
of the presence | 7 |
refuting a smt | 7 |
end of the | 7 |
and with the | 7 |
used for the | 7 |
we calculate the | 7 |
twitter users are | 7 |
tweets using the | 7 |
we chose to | 7 |
stress symptoms and | 7 |
of the content | 7 |
areas such as | 7 |
has been a | 7 |
the early days | 7 |
behavior of users | 7 |
of a user | 7 |
media as a | 7 |
is a common | 7 |
have been widely | 7 |
protection motivation theory | 7 |
aim of this | 7 |
the fuzzy accuracy | 7 |
neutral with regard | 7 |
people are more | 7 |
between april st | 7 |
predicted by higher | 7 |
the same day | 7 |
the corexq algorithm | 7 |
we believe that | 7 |
nature remains neutral | 7 |
axis represents the | 7 |
the news update | 7 |
used to extract | 7 |
if a tweet | 7 |
to assess the | 7 |
a multiple regression | 7 |
number of words | 7 |
in published maps | 7 |
the likelihood that | 7 |
using natural language | 7 |
of information and | 7 |
study is to | 7 |
this is also | 7 |
to be more | 7 |
public health concerns | 7 |
public health crisis | 7 |
used in study | 7 |
the brand equity | 7 |
allowed us to | 7 |
the era of | 7 |
the task of | 7 |
the sampling period | 7 |
the mueller investigation | 7 |
the tweets are | 7 |
to the twitter | 7 |
it is interesting | 7 |
to the information | 7 |
due to their | 7 |
that the tweet | 7 |
to answer the | 7 |
altmetric data across | 7 |
seem to be | 7 |
reported by the | 7 |
in each group | 7 |
there are a | 7 |
promoting or refuting | 7 |
likelihood of having | 7 |
of this work | 7 |
significantly from to | 7 |
has been widely | 7 |
early days of | 7 |
altmetric data in | 7 |
a machine learning | 7 |
was a significant | 7 |
tweets about covid | 7 |
the mental health | 7 |
of the italian | 7 |
study of the | 7 |
differences in the | 7 |
the last two | 7 |
do not necessarily | 7 |
period of time | 7 |
we have the | 7 |
twitter event detection | 7 |
the study period | 7 |
we focus on | 7 |
of the epidemic | 7 |
community structure in | 7 |
is a need | 7 |
been found to | 7 |
is difficult to | 7 |
patterns of covid | 7 |
the data in | 7 |
media data for | 7 |
negative matrix factorization | 7 |
divided by the | 7 |
different altmetric data | 7 |
fields of science | 7 |
based brand equity | 7 |
the introduction of | 7 |
big data technologies | 7 |
the authors have | 7 |
research on the | 7 |
such as data | 7 |
crucial role in | 7 |
we also see | 7 |
the mapreduce paradigm | 7 |
the course of | 7 |
and institutional affiliations | 7 |
the natural language | 7 |
for our analysis | 7 |
focuses on the | 7 |
same way as | 7 |
appeared to be | 7 |
to one of | 7 |
over million tweets | 7 |
to identify and | 7 |
is worth mentioning | 7 |
most frequently used | 7 |
of words associated | 7 |
st and th | 7 |
to extend the | 7 |
during the last | 7 |
to engage with | 7 |
have also been | 7 |
as an example | 7 |
sentiment classification method | 7 |
context of a | 7 |
social science research | 7 |
with machine learning | 7 |
more and more | 7 |
wang et al | 7 |
the twitter rest | 7 |
maps and institutional | 7 |
and reply tweets | 7 |
to improve the | 7 |
the sharing of | 7 |
of twitter in | 7 |
during the maintenance | 7 |
the aim of | 7 |
is available at | 7 |
is not the | 7 |
published maps and | 7 |
the retweet network | 7 |
social media user | 7 |
this may be | 7 |
and network analysis | 7 |
a framework for | 7 |
start of the | 7 |
the virus and | 7 |
centers for disease | 7 |
been used to | 7 |
the relationships between | 7 |
provided in table | 7 |
social media posts | 7 |
spread of information | 7 |
the order of | 7 |
to the virus | 7 |
for the top | 7 |
social media the | 7 |
personal negative tweet | 7 |
information such as | 7 |
and the news | 7 |
of the united | 7 |
the next step | 7 |
the outbreak of | 7 |
to evaluate the | 7 |
public health events | 7 |
of the impact | 7 |
communities in the | 7 |
was used for | 7 |
in the fields | 7 |
public coronavirus twitter | 7 |
of having seen | 7 |
in natural language | 7 |
onset of the | 7 |
social and economic | 7 |
to express their | 7 |
the public and | 7 |
of the lockdown | 7 |
the diffusion of | 7 |
for the most | 7 |
also known as | 7 |
used to predict | 7 |
time of writing | 7 |
the m s | 7 |
this is an | 7 |
sentiment analysis of | 7 |
the distributions of | 7 |
studies have been | 7 |
for altmetric data | 7 |
be that the | 7 |
springer nature remains | 7 |
is not clear | 7 |
remains neutral with | 7 |
hate speech in | 7 |
effect of the | 7 |
from the data | 7 |
the population to | 7 |
best of our | 7 |
organized as follows | 7 |
all over the | 7 |
intensity of the | 7 |
according to a | 7 |
of each user | 7 |
by using a | 7 |
the output of | 7 |
even in the | 7 |
during a health | 7 |
three disinformation items | 7 |
tweets from the | 7 |
of hot research | 7 |
the chain of | 7 |
the cov tweets | 7 |
the dissemination of | 7 |
to analyse the | 7 |
data mining and | 7 |
the subject of | 7 |
topics of discussion | 7 |
conspiracy narratives on | 7 |
the limitations of | 6 |
before and after | 6 |
in comparison to | 6 |
during the measles | 6 |
a surge in | 6 |
seems to be | 6 |
story attributed to | 6 |
indicators of the | 6 |
we collected the | 6 |
complexity of the | 6 |
in the online | 6 |
analysis of online | 6 |
any kind of | 6 |
of the communities | 6 |
due to its | 6 |
and opinion mining | 6 |
the perspective of | 6 |
in regards to | 6 |
representative of the | 6 |
political news story | 6 |
a focus on | 6 |
we provide a | 6 |
of tweets posted | 6 |
that the most | 6 |
socialdistancing and stayathome | 6 |
to be less | 6 |
themselves as more | 6 |
social media infodemic | 6 |
the advantage of | 6 |
date of the | 6 |
taking into account | 6 |
advances in social | 6 |
social media discourse | 6 |
in the public | 6 |
in table and | 6 |
as a whole | 6 |
an attempt to | 6 |
be interested in | 6 |
panic buying and | 6 |
politicians on twitter | 6 |
disaster risk reduction | 6 |
and covid information | 6 |
based on social | 6 |
the design of | 6 |
march and april | 6 |
with a strong | 6 |
that we have | 6 |
in the text | 6 |
in large networks | 6 |
information ecology framework | 6 |
of disease outbreaks | 6 |
topics on twitter | 6 |
people who are | 6 |
which has been | 6 |
to be untrue | 6 |
during the recovery | 6 |
cases of covid | 6 |
we have a | 6 |
fernandez et al | 6 |
to indicate the | 6 |
uk mps during | 6 |
the top hashtags | 6 |
in the information | 6 |
of the following | 6 |
the sum of | 6 |
piece of information | 6 |
the first time | 6 |
function of the | 6 |
a global pandemic | 6 |
table table table | 6 |
none of the | 6 |
the most dominant | 6 |
outbreak in china | 6 |
influenza epidemics using | 6 |
the management of | 6 |
brand equity model | 6 |
around the coronavirus | 6 |
unigrams and bigrams | 6 |
use of words | 6 |
remains to be | 6 |
number of increased | 6 |
set of words | 6 |
hybrid deep learning | 6 |
the titles of | 6 |
with the stories | 6 |
to see that | 6 |
has been observed | 6 |
kinds of altmetric | 6 |
as a new | 6 |
a tweet that | 6 |
number of other | 6 |
the sensitivity of | 6 |
the combination of | 6 |
we group the | 6 |
of the different | 6 |
machine learning models | 6 |
on top of | 6 |
have been proposed | 6 |
progression of the | 6 |
the details of | 6 |
as of july | 6 |
the panic buying | 6 |
of the pipeline | 6 |
order of magnitude | 6 |
media activities of | 6 |
we compare the | 6 |
and the network | 6 |
a range of | 6 |
the altmetric data | 6 |
results in the | 6 |
users who have | 6 |
working from home | 6 |
on twitter date | 6 |
as an early | 6 |
early detection of | 6 |
the proposed algorithm | 6 |
the extraction of | 6 |
rated likelihood of | 6 |
the news timeline | 6 |
to refer to | 6 |
infected by the | 6 |
the most relevant | 6 |
application programming interface | 6 |
with social media | 6 |
media briefing on | 6 |
word in the | 6 |
applied to the | 6 |
those concerned with | 6 |
as more likely | 6 |
the lens of | 6 |
the sentiment of | 6 |
of the stimulus | 6 |
topics such as | 6 |
the degree sequence | 6 |
is needed to | 6 |
staying at home | 6 |
within the same | 6 |
on the twitter | 6 |
for depression detection | 6 |
towards uk politicians | 6 |
information can be | 6 |
of interest to | 6 |
detection on social | 6 |
version posted june | 6 |
the main topics | 6 |
of the time | 6 |
note that this | 6 |
have been applied | 6 |
a framework to | 6 |
detecting and tracking | 6 |
at the media | 6 |
a large scale | 6 |
ways in which | 6 |
during different stages | 6 |
emerging infectious disease | 6 |
pandemic and the | 6 |
by the virus | 6 |
used as the | 6 |
use of a | 6 |
to conspiracy narratives | 6 |
impact on the | 6 |
time of this | 6 |
first week of | 6 |
the politics of | 6 |
group the hashtags | 6 |
the hashtags into | 6 |
twitter is a | 6 |
on the use | 6 |
ticket sales and | 6 |
the duration of | 6 |
the conservative party | 6 |
replies to mps | 6 |
in a more | 6 |
the lda model | 6 |
words from the | 6 |
large amount of | 6 |
the most active | 6 |
h n pandemic | 6 |
dynamics of hate | 6 |
concerned with fraud | 6 |
opening remarks at | 6 |
subject fields of | 6 |
and firm value | 6 |
twitter mentions and | 6 |
the coronavirus disease | 6 |
to have been | 6 |
we collect data | 6 |
take care of | 6 |
and data mining | 6 |
of twitter to | 6 |
promoting and refuting | 6 |
the initial tweet | 6 |
and news media | 6 |
spread of misinformation | 6 |
machine learning techniques | 6 |
hashtags into six | 6 |
if there is | 6 |
and how it | 6 |
has been applied | 6 |
used by public | 6 |
are a number | 6 |
of tweets was | 6 |
and other social | 6 |
discussed in chapter | 6 |
step sentiment classification | 6 |
game attendance and | 6 |
for those who | 6 |
the next section | 6 |
an eid outbreak | 6 |
in these countries | 6 |
on the left | 6 |
there are some | 6 |
view of the | 6 |
core of the | 6 |
results showed that | 6 |
so that we | 6 |
appear in the | 6 |
that the stories | 6 |
be used for | 6 |
available in the | 6 |
to detect and | 6 |
we note that | 6 |
on twitter is | 6 |
in the era | 6 |
there are multiple | 6 |
hate speech is | 6 |
we present a | 6 |
make sense of | 6 |
it has also | 6 |
play a crucial | 6 |
the party in | 6 |
of each word | 6 |
to generate the | 6 |
of twitter and | 6 |
through the lens | 6 |
association for the | 6 |
in all the | 6 |
of the spread | 6 |
here we see | 6 |
we obtain the | 6 |
to engage in | 6 |
of news and | 6 |
in the city | 6 |
figure shows that | 6 |
sources such as | 6 |
to collect data | 6 |
a political news | 6 |
the benefits of | 6 |
important to note | 6 |
for each day | 6 |
as a news | 6 |
social network sites | 6 |
the paper is | 6 |
the sentiment classification | 6 |
we counted the | 6 |
and the social | 6 |
of confirmed cases | 6 |
of the nodes | 6 |
is essential to | 6 |
that did not | 6 |
and understanding of | 6 |
on the one | 6 |
one or more | 6 |
of a public | 6 |
sentiment of the | 6 |
for the same | 6 |
understand the chain | 6 |
other altmetric data | 6 |
data and the | 6 |
the potential for | 6 |
in the face | 6 |
the similarity between | 6 |
on the political | 6 |
semantic role labeling | 6 |
and fake news | 6 |
across all the | 6 |
the most commonly | 6 |
to may th | 6 |
similar research topics | 6 |
the users who | 6 |
in this way | 6 |
more than times | 6 |
detection on twitter | 6 |
of facebook use | 6 |
in the table | 6 |
public health surveillance | 6 |
a method to | 6 |
in the absence | 6 |
different political parties | 6 |
to model the | 6 |
by the fact | 6 |
peaks of moc | 6 |
to see how | 6 |
related to this | 6 |
hashtag groups and | 6 |
by the users | 6 |
number of nodes | 6 |
of the dataset | 6 |
based on this | 6 |
right wing community | 6 |
comparison of the | 6 |
an early warning | 6 |
untrue political stories | 6 |
during hurricane sandy | 6 |
of tweets is | 6 |
to address this | 6 |
a higher likelihood | 6 |
would be more | 6 |
the very first | 6 |
reported a greater | 6 |
a need to | 6 |
information regarding the | 6 |
and intensity of | 6 |
could be a | 6 |
fields and research | 6 |
and of the | 6 |
in the middle | 6 |
given that the | 6 |
the extent of | 6 |
a survey on | 6 |
figure and figure | 6 |
team brand equity | 6 |
bigru and cnn | 6 |
the core of | 6 |
due to a | 6 |
there may be | 6 |
of a resilient | 6 |
the most prominent | 6 |
was able to | 6 |
owing to the | 6 |
six main categories | 6 |
hundreds of thousands | 6 |
of the coronavirus | 6 |
confirmed cases in | 6 |
in some cases | 6 |
of the brand | 6 |
weaker relationship between | 6 |
the most used | 6 |
which they are | 6 |
in each of | 6 |
our goal is | 6 |
and quote tweets | 6 |
this can be | 6 |
different social media | 6 |
the virus is | 6 |
the uncertainty of | 6 |
data for this | 6 |
used in social | 6 |
of data presence | 6 |
are positively related | 6 |
period of data | 6 |
did not have | 6 |
the attention of | 6 |
like to thank | 6 |
the semantic networks | 6 |
toward uk mps | 6 |
presented in the | 6 |
proportion of the | 6 |
between these two | 6 |
was carried out | 6 |
assets are positively | 6 |
and the average | 6 |
data sources are | 6 |
we developed a | 6 |
between democrats and | 6 |
facebook and twitter | 6 |
for further analysis | 6 |
attributed to source | 6 |
the term frequency | 6 |
communities in large | 6 |
the names of | 6 |
associated with self | 6 |
well as a | 6 |
the tweet contains | 6 |
the prevalence of | 6 |
new york city | 6 |
conflicts of interest | 6 |
network of the | 6 |
presented in this | 6 |
over the world | 6 |
abuse toward mps | 6 |
and sentiment analysis | 6 |
of stakeholder value | 6 |
we analyzed the | 6 |
they are not | 6 |
in major league | 6 |
from search were | 6 |
measles outbreak in | 6 |
a portion of | 6 |
the city of | 6 |
has not been | 6 |
is lower than | 6 |
positive for covid | 6 |
of novel coronavirus | 6 |
of data and | 6 |
rather than facebook | 6 |
the dataset is | 6 |
analysis of data | 6 |
using twitter and | 6 |
american society of | 6 |
to this end | 6 |
of the sample | 6 |
a description of | 6 |
a weaker relationship | 6 |
reported in the | 6 |
and information diffusion | 6 |
times higher than | 6 |
statistics of the | 6 |
belief that the | 6 |
public health agencies | 6 |
indicates that the | 6 |
tended to be | 6 |
the hypothesis that | 6 |
an alternative to | 6 |
in the top | 6 |
story online that | 6 |
that the majority | 6 |
seeded lda model | 6 |
order to understand | 6 |
for users who | 6 |
in the nyt | 6 |
probability of observing | 6 |
regard to the | 6 |
of profanity words | 6 |
social media by | 6 |
receiving a high | 6 |
the current work | 6 |
the real network | 6 |
to be seen | 6 |
for the following | 6 |
harmful media coverage | 6 |
of tweets from | 6 |
interested in the | 6 |
was followed by | 6 |
this paper presents | 6 |
control and prevention | 6 |
is useful for | 6 |
we present our | 6 |
the pandemic on | 6 |
the death nls | 6 |
policy makers and | 6 |
to check the | 6 |
the situation in | 6 |
addition to that | 6 |
to public health | 6 |
missile test surge | 6 |
the streaming api | 6 |
in contact with | 6 |
chain of events | 6 |
the network and | 6 |
in consideration of | 6 |
to classify the | 6 |
score for each | 6 |
briefing on covid | 6 |
of the web | 6 |
the web page | 6 |
for both the | 6 |
news story online | 6 |
for all tweets | 6 |
remarks at the | 6 |
the face of | 6 |
the twitter stream | 6 |
from the dataset | 6 |
these tweets were | 6 |
if the account | 6 |
which do not | 6 |
example of a | 6 |
to address the | 6 |
number of unique | 6 |
from further analysis | 6 |
compared with the | 6 |
that many people | 6 |
to filter out | 6 |
who did not | 6 |
based on different | 6 |
composed by the | 6 |
a corpus of | 6 |
in contrast to | 6 |
we would like | 6 |
the intensity of | 6 |
measure of the | 6 |
presented in table | 6 |
into six main | 6 |
seriousness of the | 6 |
public health crises | 6 |
public health and | 6 |
the media briefing | 6 |
detection and tracking | 6 |
of big data | 6 |
cloud in fig | 6 |
this measure is | 6 |
the framing of | 6 |
with madrid and | 6 |
each pair of | 6 |
mental health problems | 6 |
used by the | 6 |
to be an | 6 |
according to their | 6 |
cwts classification system | 6 |
the progression of | 6 |
in the original | 6 |
an original tweet | 6 |
generated by the | 6 |
pneumonia in wuhan | 6 |
the source of | 6 |
of information diffusion | 6 |
the understanding of | 6 |
media data sources | 6 |
in the process | 6 |
identify hot research | 6 |
performance of our | 6 |
tweets based on | 5 |
the two most | 5 |
we applied the | 5 |
presence of different | 5 |
for our model | 5 |
a timely manner | 5 |
is dedicated to | 5 |
authoritativeness of the | 5 |
the lockdown period | 5 |
was not certified | 5 |
by the twitter | 5 |
it is labeled | 5 |
connected to the | 5 |
the differences in | 5 |
retweets and favorites | 5 |
each tweet was | 5 |
risk perception and | 5 |
the data sources | 5 |
and political frame | 5 |
and health sciences | 5 |
is likely that | 5 |
the right wing | 5 |
study found that | 5 |
the news media | 5 |
tweets from march | 5 |
a content analysis | 5 |
explained by the | 5 |
have been conducted | 5 |
the pew research | 5 |
sharing of untrue | 5 |
users and the | 5 |
retrieved from the | 5 |
was done on | 5 |
importance of the | 5 |
media platforms such | 5 |
data as social | 5 |
information and communication | 5 |
alpha for this | 5 |
a linguistic analysis | 5 |
those who have | 5 |
analysis and the | 5 |
to look at | 5 |
public health organizations | 5 |
and use of | 5 |
the first and | 5 |
and higher levels | 5 |
participant demographics are | 5 |
is followed by | 5 |
to test the | 5 |
the results for | 5 |
the communication ecosystem | 5 |
of the infection | 5 |
to collect and | 5 |
by the disease | 5 |
the different methods | 5 |
we study the | 5 |
of tweets containing | 5 |
found out was | 5 |
hall of fame | 5 |
and do not | 5 |
big data from | 5 |
were also more | 5 |
to detect depression | 5 |
german speaking twitter | 5 |
most common symptoms | 5 |
we observed that | 5 |
of data for | 5 |
people were more | 5 |
of the daily | 5 |
between citations and | 5 |
this suggests that | 5 |
in social big | 5 |
list of words | 5 |
average number of | 5 |
the title of | 5 |
most commonly used | 5 |
is the total | 5 |
an important issue | 5 |
done on twitter | 5 |
over the course | 5 |
asked to rate | 5 |
model with the | 5 |
ranged from to | 5 |
to tweet about | 5 |
of twitter messages | 5 |
of tweets by | 5 |
on twitter about | 5 |
in figure and | 5 |
set of users | 5 |
for the surgery | 5 |
based on our | 5 |
social media twitter | 5 |
and the peaks | 5 |
machine learning on | 5 |
a selection of | 5 |
data sources for | 5 |
in the geocov | 5 |
social media metrics | 5 |
to control the | 5 |
smith et al | 5 |
not certified by | 5 |
within the network | 5 |
online engagement and | 5 |
the school closures | 5 |
social learning and | 5 |
this was done | 5 |
better understanding of | 5 |
by peer review | 5 |
of information to | 5 |
surgery of trauma | 5 |
to be able | 5 |
the social web | 5 |
to slow the | 5 |
had seen them | 5 |
of replies received | 5 |
from the center | 5 |
home save lives | 5 |
were collected from | 5 |
this is that | 5 |
reporting a higher | 5 |
of which are | 5 |
the analysis results | 5 |
fast unfolding of | 5 |
of using social | 5 |
political polarization on | 5 |
of communities in | 5 |
have more than | 5 |
contents of the | 5 |
patterns of information | 5 |
the original tweet | 5 |
tweets originating from | 5 |
news and the | 5 |
set of publications | 5 |
in this research | 5 |
can find the | 5 |
in the em | 5 |
methods used by | 5 |
of the relevant | 5 |
can be accessed | 5 |
is the set | 5 |
the tweets collected | 5 |
is clear that | 5 |
this could be | 5 |
algorithm to detect | 5 |
influenced by the | 5 |
no conflict of | 5 |
rise of social | 5 |
increase the impact | 5 |
and support vector | 5 |
in which each | 5 |
the results presented | 5 |
coverage on the | 5 |
later found out | 5 |
as far as | 5 |
in each panel | 5 |
stated that they | 5 |
quantitatively and qualitatively | 5 |
in the philippines | 5 |
that they have | 5 |
on the whole | 5 |
consistent with their | 5 |
not only the | 5 |
between the death | 5 |
involved in the | 5 |
detection from twitter | 5 |
to contribute to | 5 |
title of the | 5 |
to be shared | 5 |
the control group | 5 |
the detailed information | 5 |
the contents of | 5 |
the twitter posts | 5 |
and big data | 5 |
who do not | 5 |
there are many | 5 |
there are micro | 5 |
notation description see | 5 |
we considered the | 5 |
of our study | 5 |
media in the | 5 |
to establish whether | 5 |
analysis and opinion | 5 |
we have developed | 5 |
but do not | 5 |
social network data | 5 |
distribution with a | 5 |
a very skewed | 5 |
considerable amount of | 5 |
whether the same | 5 |
cough and fever | 5 |
of marketing assets | 5 |
well as for | 5 |
we remove the | 5 |
results from the | 5 |
a platform for | 5 |
of the opposition | 5 |
true and false | 5 |
have developed a | 5 |
the real world | 5 |
most frequent words | 5 |
in our data | 5 |
is focused on | 5 |
in a timely | 5 |
public opinion and | 5 |
from the perspective | 5 |
had a very | 5 |
stay home save | 5 |
the difference between | 5 |
on advances in | 5 |
analysis and topic | 5 |
a very large | 5 |
be an important | 5 |
set of features | 5 |
of the algorithm | 5 |
a personal negative | 5 |
a link between | 5 |
the personal versus | 5 |
identifying hot research | 5 |
sections of the | 5 |
known as the | 5 |
machine learning model | 5 |
a period of | 5 |
for data collection | 5 |
not only to | 5 |
representation of a | 5 |
related stress symptoms | 5 |
to the original | 5 |
the second most | 5 |
in table are | 5 |
in this area | 5 |
january to april | 5 |
and analysis of | 5 |
as a proxy | 5 |
natural language toolkit | 5 |
media data mining | 5 |
to assign a | 5 |
at this stage | 5 |
networks can be | 5 |
between tweets and | 5 |
of tweets for | 5 |
social networks have | 5 |
twitter data and | 5 |
quantitative analysis of | 5 |
of the studies | 5 |
korea missile test | 5 |
likely to engage | 5 |
that it does | 5 |
this time period | 5 |
a sequence of | 5 |
law enforcement agencies | 5 |
big data analysis | 5 |
the population of | 5 |
outbreak in the | 5 |
neural networks and | 5 |
but does not | 5 |
of twitter accounts | 5 |
working hard to | 5 |
number of twitter | 5 |
of tweets to | 5 |
events related to | 5 |
that twitter can | 5 |
patterns in the | 5 |
these types of | 5 |
the bipartite directed | 5 |
the trend of | 5 |
the speed of | 5 |
of the cases | 5 |
the propagation of | 5 |
is composed of | 5 |
of the media | 5 |
data can be | 5 |
data for research | 5 |
wide number of | 5 |
the th of | 5 |
issues such as | 5 |
male or female | 5 |
share of the | 5 |
results of this | 5 |
power to detect | 5 |
the different hashtag | 5 |
to access the | 5 |
used to study | 5 |
will need to | 5 |
twitter sentiment classification | 5 |
network analysis to | 5 |
row of panels | 5 |
have a higher | 5 |
about the covid | 5 |
to keep the | 5 |
a collection of | 5 |
during the pre | 5 |
large amounts of | 5 |
english language tweets | 5 |
high number of | 5 |
included in this | 5 |
a predictor variable | 5 |
on the platform | 5 |
to illustrate the | 5 |
they are likely | 5 |
be useful for | 5 |
subset of the | 5 |
the accuracy assessment | 5 |
on the right | 5 |
of h b | 5 |
was supported by | 5 |
were found to | 5 |
word cloud on | 5 |
any data analysis | 5 |
a network of | 5 |
data in a | 5 |
the training dataset | 5 |
to describe the | 5 |
appearing in the | 5 |
the information ecology | 5 |
a wide number | 5 |
the big data | 5 |
to our knowledge | 5 |
of artificial intelligence | 5 |
have been designed | 5 |
effect of social | 5 |
pairs of words | 5 |
the numbers of | 5 |
but it is | 5 |
of social learning | 5 |
also more likely | 5 |
can be observed | 5 |
history of the | 5 |
respect to the | 5 |
summary of the | 5 |
twitter data can | 5 |
the cwts classification | 5 |
the tweet ids | 5 |
with the following | 5 |
and the use | 5 |
of the weber | 5 |
in figure a | 5 |
considered as a | 5 |
the coronavirus topic | 5 |
for this analysis | 5 |
are included in | 5 |
is vital to | 5 |
collected twitter data | 5 |
can be difficult | 5 |
linked to the | 5 |
the pattern of | 5 |
was assigned to | 5 |
media coverage and | 5 |
states during the | 5 |
a tweet was | 5 |
of an outbreak | 5 |
paper is organized | 5 |
consistent with our | 5 |
a specific disease | 5 |
frames used by | 5 |
in understanding the | 5 |
information provided by | 5 |
the extension of | 5 |
excluded from further | 5 |
the previous subsection | 5 |
between political parties | 5 |
in both the | 5 |
deep learning for | 5 |
conference on advances | 5 |
the research topic | 5 |
we can also | 5 |
been widely used | 5 |
increasing use of | 5 |
can be described | 5 |
during the lockdown | 5 |
the tweets into | 5 |
has been shown | 5 |
titles of the | 5 |
that they were | 5 |
most of these | 5 |
we collected all | 5 |
to characterize the | 5 |
of altmetric events | 5 |
magnitude of the | 5 |
to show that | 5 |
false political stories | 5 |
from the user | 5 |
they can be | 5 |
point in time | 5 |
has shown that | 5 |
which is an | 5 |
to examine how | 5 |
networks have become | 5 |
of the best | 5 |
small number of | 5 |
on the map | 5 |
it remains to | 5 |
tweets per day | 5 |
illustrated in figure | 5 |
exposure to the | 5 |
for early detection | 5 |
also used to | 5 |
to see the | 5 |
for people to | 5 |
levels of social | 5 |
the news coverage | 5 |
on twitter using | 5 |
the resulting network | 5 |
does not contain | 5 |
the time was | 5 |
table and fig | 5 |
cloud on the | 5 |
and showed that | 5 |
could also be | 5 |
of the situation | 5 |
females were more | 5 |
section of the | 5 |
a matter of | 5 |
unfolding of communities | 5 |
features of twitter | 5 |
sum of the | 5 |
of different altmetric | 5 |
has become a | 5 |