This is a table of type bigram and their frequencies. Use it to search & browse the list to learn more about your study carrel.
bigram | frequency |
---|---|
social media | 890 |
machine learning | 212 |
public health | 170 |
altmetric data | 152 |
twitter data | 132 |
twitter users | 126 |
social networks | 110 |
big data | 107 |
research topics | 104 |
united states | 97 |
social distancing | 85 |
brand equity | 81 |
sentiment analysis | 81 |
hate speech | 79 |
social network | 75 |
marketing assets | 59 |
media data | 58 |
using twitter | 57 |
data collection | 57 |
data mining | 56 |
media coverage | 56 |
doc id | 54 |
natural language | 54 |
cord uid | 54 |
fake news | 53 |
network analysis | 52 |
media platforms | 51 |
local tv | 49 |
data analysis | 49 |
hot research | 47 |
information diffusion | 45 |
mental health | 45 |
higher levels | 44 |
total number | 43 |
event detection | 43 |
conspiracy narratives | 42 |
topic modeling | 41 |
language processing | 41 |
data sources | 40 |
news frames | 40 |
table shows | 39 |
tv viewership | 38 |
sentiment classification | 36 |
twitter activity | 36 |
million tweets | 35 |
financial performance | 35 |
negative tweets | 34 |
game attendance | 34 |
time period | 34 |
news media | 34 |
personal negative | 34 |
online social | 33 |
deep learning | 33 |
new york | 32 |
verified users | 32 |
abusive replies | 32 |
content analysis | 31 |
twitter api | 31 |
using social | 31 |
time series | 30 |
twitter followers | 30 |
world health | 30 |
see table | 30 |
political orientation | 29 |
data presence | 29 |
health organization | 29 |
disease outbreaks | 28 |
twitter dataset | 28 |
stakeholder value | 28 |
novel coronavirus | 28 |
panic buying | 28 |
case study | 28 |
tweets dataset | 27 |
tweets containing | 27 |
made available | 27 |
author funder | 27 |
granted medrxiv | 27 |
right wing | 27 |
twitter mentions | 27 |
nan doi | 27 |
information operations | 27 |
political parties | 26 |
international license | 26 |
health crisis | 26 |
subject fields | 26 |
media use | 25 |
text mining | 25 |
peer review | 25 |
validated network | 25 |
related tweets | 25 |
media literacy | 25 |
copyright holder | 25 |
statistically significant | 25 |
social learning | 25 |
semantic network | 24 |
boris johnson | 24 |
home schooling | 24 |
personal tweets | 24 |
social big | 24 |
streaming api | 24 |
media users | 24 |
conspiracy theories | 24 |
twitter accounts | 24 |
abusive language | 23 |
personal non | 23 |
accuracy assessment | 23 |
ticket sales | 23 |
coronavirus disease | 23 |
team financial | 23 |
news tweet | 23 |
confirmed cases | 22 |
school closures | 22 |
original tweets | 22 |
use social | 22 |
diffusion patterns | 22 |
depression detection | 22 |
artificial intelligence | 22 |
infectious disease | 22 |
news coverage | 22 |
data set | 22 |
twitter posts | 22 |
uk mps | 22 |
publicly available | 22 |
risk perception | 21 |
social networking | 21 |
communication ecosystem | 21 |
retweet network | 21 |
among others | 21 |
gender differences | 21 |
tweets using | 21 |
new media | 20 |
law enforcement | 20 |
nan sha | 20 |
latent dirichlet | 20 |
future research | 20 |
topic modelling | 20 |
league baseball | 20 |
domestic abuse | 20 |
public opinion | 20 |
el mundo | 20 |
sir model | 20 |
international conference | 20 |
major league | 20 |
hashtag groups | 20 |
depressed users | 20 |
version posted | 20 |
label propagation | 20 |
per day | 20 |
tweets posted | 20 |
word embedding | 19 |
deaths per | 19 |
seasonal ma | 19 |
english tweets | 19 |
real time | 19 |
location information | 19 |
tweet ids | 19 |
news articles | 19 |
may also | 19 |
twitter use | 19 |
two different | 18 |
donald trump | 18 |
research question | 18 |
regression models | 18 |
dirichlet allocation | 18 |
historical sharing | 18 |
digital platforms | 18 |
stress symptoms | 18 |
crisis communication | 18 |
state sponsored | 18 |
abuse levels | 18 |
measles outbreak | 18 |
smt immunity | 18 |
least one | 18 |
immunity link | 18 |
privacy concerns | 18 |
previous studies | 18 |
community detection | 18 |
large number | 17 |
health care | 17 |
across different | 17 |
twitter sentiment | 17 |
word embeddings | 17 |
twitter messages | 17 |
word co | 17 |
president trump | 17 |
time periods | 17 |
methods used | 17 |
dependent variable | 17 |
twitter account | 17 |
high level | 17 |
learning algorithms | 17 |
frames used | 16 |
data processing | 16 |
digital media | 16 |
general covid | 16 |
disaster management | 16 |
moral panics | 16 |
twitter user | 16 |
personal tweet | 16 |
words related | 16 |
bipartite network | 16 |
may th | 16 |
personal versus | 16 |
false information | 16 |
news tweets | 16 |
tweets related | 16 |
even though | 16 |
information ecology | 16 |
supplementary material | 16 |
commonly used | 16 |
epidemic intelligence | 16 |
high levels | 16 |
psychophysical numbing | 16 |
moral panic | 16 |
neural networks | 16 |
semantic networks | 16 |
open access | 15 |
public concern | 15 |
swine flu | 15 |
results show | 15 |
text analysis | 15 |
early stages | 15 |
data source | 15 |
seeded lda | 15 |
topic models | 15 |
literature review | 15 |
collect data | 15 |
two weeks | 15 |
conspiracy theory | 15 |
information cascades | 15 |
hurricane sandy | 15 |
political party | 15 |
different types | 15 |
unified theory | 15 |
medrxiv preprint | 15 |
widely used | 15 |
abuse toward | 15 |
risk communication | 15 |
classification method | 15 |
different stages | 15 |
fechner law | 15 |
support vector | 15 |
infectious diseases | 15 |
tweets per | 14 |
digital literacy | 14 |
information sharing | 14 |
bot detection | 14 |
see appendix | 14 |
quote tweets | 14 |
prime minister | 14 |
lda model | 14 |
covid information | 14 |
data hunger | 14 |
news frame | 14 |
user behaviour | 14 |
early warning | 14 |
political frame | 14 |
subject field | 14 |
recent years | 14 |
season marketing | 14 |
mining techniques | 14 |
political polarization | 14 |
english language | 14 |
resilient communication | 14 |
language tweets | 14 |
power law | 14 |
data across | 14 |
retweet prediction | 14 |
collected tweets | 14 |
action words | 14 |
previous research | 14 |
versus news | 14 |
posted may | 14 |
via twitter | 13 |
first step | 13 |
reported likelihood | 13 |
research questions | 13 |
tweets collected | 13 |
based approach | 13 |
lower levels | 13 |
hashtag group | 13 |
corexq algorithm | 13 |
original tweet | 13 |
negative versus | 13 |
health information | 13 |
topic level | 13 |
altmetric events | 13 |
versus non | 13 |
trend analysis | 13 |
medical education | 13 |
general public | 13 |
opinion mining | 13 |
disease surveillance | 13 |
th inclusive | 13 |
learning model | 13 |
health events | 13 |
learning methods | 13 |
data coverage | 13 |
mass media | 13 |
raw data | 13 |
maintenance stage | 13 |
word cloud | 13 |
community structure | 13 |
virtue signalling | 13 |
supplementary information | 13 |
online abuse | 13 |
potential reach | 13 |
table gives | 13 |
hunter tier | 13 |
different methods | 13 |
wos publications | 13 |
negative tweet | 13 |
seasonal marketing | 13 |
twitter stream | 13 |
large scale | 13 |
mendeley readers | 13 |
twitter content | 13 |
italia viva | 13 |
social science | 13 |
sponsored media | 12 |
data sets | 12 |
refuting tweets | 12 |
jaccard coefficient | 12 |
april th | 12 |
many people | 12 |
two categories | 12 |
future work | 12 |
sample size | 12 |
health frame | 12 |
two types | 12 |
political news | 12 |
false material | 12 |
highest number | 12 |
policy document | 12 |
sentiment score | 12 |
social cybersecurity | 12 |
via social | 12 |
tweet text | 12 |
media messages | 12 |
detection using | 12 |
see section | 12 |
learning systems | 12 |
three different | 12 |
news update | 12 |
key words | 12 |
social web | 12 |
non reputable | 12 |
false news | 12 |
neural network | 12 |
negative sentiment | 12 |
also used | 12 |
million users | 12 |
twitter provides | 12 |
natural disasters | 12 |
firm value | 12 |
networking sites | 12 |
replies received | 12 |
related terms | 12 |
geocov tweets | 12 |
facebook mentions | 12 |
social data | 12 |
media activities | 12 |
political discourse | 12 |
modal features | 12 |
shifting individuals | 12 |
steel blue | 12 |
online hate | 12 |
linguistic analysis | 12 |
previous work | 12 |
public discourse | 12 |
toilet paper | 12 |
climate change | 12 |
across subject | 11 |
recovery period | 11 |
learning models | 11 |
early days | 11 |
health research | 11 |
collected data | 11 |
frequently used | 11 |
tested positive | 11 |
less likely | 11 |
mendeley readership | 11 |
fuzzy accuracy | 11 |
march th | 11 |
update frame | 11 |
every day | 11 |
reply tweets | 11 |
crime prevention | 11 |
news mentions | 11 |
vaccine frame | 11 |
one another | 11 |
presidential election | 11 |
per number | 11 |
words associated | 11 |
exploratory analysis | 11 |
mental illness | 11 |
public perception | 11 |
also observe | 11 |
expanded analysis | 11 |
different political | 11 |
analysis methods | 11 |
political stories | 11 |
two main | 11 |
logistic regression | 11 |
york times | 11 |
public healthcare | 11 |
global pandemic | 11 |
topic model | 11 |
public sentiment | 11 |
mention network | 11 |
team financials | 11 |
abc news | 11 |
united kingdom | 11 |
will also | 11 |
common words | 11 |
training data | 11 |
automated accounts | 11 |
mainstream media | 11 |
tw i | 11 |
based method | 11 |
news classification | 11 |
news sources | 11 |
unverified users | 11 |
social bots | 11 |
official accounts | 11 |
untrue material | 10 |
news count | 10 |
personality traits | 10 |
different countries | 10 |
early detection | 10 |
different social | 10 |
related words | 10 |
marketing asset | 10 |
based approaches | 10 |
disease outbreak | 10 |
use twitter | 10 |
red line | 10 |
research areas | 10 |
pattern recognition | 10 |
remote learning | 10 |
following tweet | 10 |
us presidential | 10 |
may lead | 10 |
phrase i | 10 |
blogs citations | 10 |
depression symptoms | 10 |
worth noting | 10 |
false positives | 10 |
large networks | 10 |
diffusion dynamics | 10 |
public response | 10 |
descriptive statistics | 10 |
information retrieval | 10 |
standard deviation | 10 |
season ma | 10 |
hate generation | 10 |
may help | 10 |
research topic | 10 |
open source | 10 |
many tweets | 10 |
word clouds | 10 |
main categories | 10 |
allows us | 10 |
th april | 10 |
april st | 10 |
confusion matrix | 10 |
statistically significantly | 10 |
also show | 10 |
emerging infectious | 10 |
analysis using | 10 |
time frame | 10 |
vast majority | 10 |
experimental results | 10 |
daily number | 10 |
health issues | 10 |
media platform | 10 |
abuse level | 10 |
table presents | 10 |
health professional | 10 |
speech detection | 10 |
classification system | 10 |
vector machine | 10 |
will use | 9 |
media twitter | 9 |
analysis techniques | 9 |
disease control | 9 |
policy makers | 9 |
profanity words | 9 |
saudi arabia | 9 |
clustering algorithm | 9 |
conspiracy beliefs | 9 |
named entities | 9 |
analysis results | 9 |
political events | 9 |
higher likelihood | 9 |
third party | 9 |
presence across | 9 |
four studies | 9 |
time window | 9 |
table table | 9 |
wing parties | 9 |
crisis period | 9 |
agenda setting | 9 |
seed words | 9 |
labour politicians | 9 |
text data | 9 |
around covid | 9 |
directed validated | 9 |
exponential growth | 9 |
two periods | 9 |
also found | 9 |
government agencies | 9 |
death rate | 9 |
authors also | 9 |
exogenous signals | 9 |
positively related | 9 |
thematic analysis | 9 |
retweet cascades | 9 |
protective equipment | 9 |
active users | 9 |
higher extraversion | 9 |
twitter rest | 9 |
news timeline | 9 |
topic detection | 9 |
one study | 9 |
example tweets | 9 |
negative classification | 9 |
news detection | 9 |
verified user | 9 |
new information | 9 |
rest api | 9 |
popular hashtags | 9 |
crisis management | 9 |
european union | 9 |
lockdown period | 9 |
specific altmetric | 9 |
lower agreeableness | 9 |
better understand | 9 |
uk news | 9 |
first two | 9 |
trending hashtags | 9 |
systematic review | 9 |
also known | 9 |
detecting influenza | 9 |
surveillance system | 9 |
twitter using | 9 |
gives us | 9 |
abuse sent | 9 |
different altmetric | 9 |
shared untrue | 9 |
uk politicians | 9 |
cnn model | 9 |
usa states | 9 |
online news | 9 |
tweets concerned | 9 |
text classification | 9 |
unique users | 9 |
cov tweets | 9 |
last two | 9 |
pew research | 9 |
based methods | 9 |
features representing | 9 |
naive bayes | 9 |
different time | 9 |
current work | 9 |
health crises | 9 |
research outputs | 9 |
document citations | 9 |
data points | 9 |
disaster risk | 9 |
centrality metrics | 9 |
virus spread | 9 |
information warfare | 9 |
stay home | 9 |
scientific publications | 9 |
multiple regression | 9 |
outbreak detection | 9 |
quantitative analysis | 9 |
future studies | 8 |
verified twitter | 8 |
evacuation orders | 8 |
sentiment polarity | 8 |
disaster response | 8 |
clinical scenarios | 8 |
phq value | 8 |
terms used | 8 |
keystone species | 8 |
learning framework | 8 |
greater likelihood | 8 |
risk reduction | 8 |
zero values | 8 |
available data | 8 |
jeremy corbyn | 8 |
related hashtags | 8 |
disinformation stories | 8 |
omitted variables | 8 |
sharing disinformation | 8 |
authors declare | 8 |
diversionary tweets | 8 |
activity history | 8 |
file system | 8 |
mapreduce paradigm | 8 |
news source | 8 |
learning approach | 8 |
allows users | 8 |
last years | 8 |
one million | 8 |
will change | 8 |
twitter event | 8 |
also see | 8 |
political authority | 8 |
spinal manipulation | 8 |
mlb teams | 8 |
generated content | 8 |
tweets mentioning | 8 |
least squares | 8 |
covid crisis | 8 |
skewed distribution | 8 |
hateful tweets | 8 |
main topics | 8 |
disinformation items | 8 |
many times | 8 |
review comments | 8 |
relatively small | 8 |
modalities attribute | 8 |
twitter streaming | 8 |
online engagement | 8 |
twitter date | 8 |
healthcare workers | 8 |
study period | 8 |
prominent news | 8 |
relevant information | 8 |
non seasonal | 8 |
public concerns | 8 |
initial stage | 8 |
influenza epidemics | 8 |
web data | 8 |
word count | 8 |
related stress | 8 |
information regarding | 8 |
media manipulation | 8 |
stop words | 8 |
relatively high | 8 |
information network | 8 |
per one | 8 |
pandemic update | 8 |
sky blue | 8 |
allow users | 8 |
meaning accretion | 8 |
news headlines | 8 |
word frequency | 8 |
different data | 8 |
information technology | 8 |
across social | 8 |
similar research | 8 |
results also | 8 |
based classification | 8 |
user experiences | 8 |
user engagement | 8 |
twitter communications | 8 |
real world | 8 |
quote tweet | 8 |
marketing analytics | 8 |
textual data | 8 |
decision support | 8 |
echo chambers | 8 |
social sciences | 8 |
regression analysis | 8 |
per post | 8 |
pilot work | 8 |
positive relationship | 8 |
different sources | 8 |
tweets promoting | 8 |
closely related | 8 |
occurrence networks | 8 |
significantly predicted | 8 |
mlb brand | 8 |
data collected | 8 |
epidemic outbreaks | 8 |
profanity list | 8 |
sampling period | 8 |
million people | 8 |
term frequency | 8 |
journal clubs | 8 |
negative score | 8 |
average sentiment | 8 |
visual analytics | 8 |
relatively low | 8 |
behavior analysis | 8 |
training dataset | 8 |
news story | 8 |
spatial sensors | 8 |
media sites | 8 |
user tweets | 8 |
three disinformation | 8 |
significantly higher | 8 |
citation data | 8 |
social world | 8 |
endoscopic evaluation | 8 |
data analytics | 8 |
news reports | 8 |
qualitative analysis | 8 |
media user | 8 |
personal protective | 8 |
moderating effect | 8 |
police forces | 8 |
reported symptoms | 8 |
typhoon hagibis | 7 |
left wing | 7 |
one hundred | 7 |
language use | 7 |
next section | 7 |
south korea | 7 |
targeted analysis | 7 |
tweet dataset | 7 |
topics related | 7 |
regression model | 7 |
using different | 7 |
respiratory syndrome | 7 |
programming interface | 7 |
published maps | 7 |
inverse document | 7 |
related corpora | 7 |
personal information | 7 |
professional sports | 7 |
sporting events | 7 |
mueller coverage | 7 |
user will | 7 |
social change | 7 |
related information | 7 |
public coronavirus | 7 |
hybrid deep | 7 |
arabic tweets | 7 |
blue community | 7 |
verbal abuse | 7 |
axis represents | 7 |
tweets may | 7 |
bipartite directed | 7 |
existing beliefs | 7 |
worth mentioning | 7 |
twitter network | 7 |
general locations | 7 |
table i | 7 |
policy documents | 7 |
pattern pool | 7 |
topics generated | 7 |
health concerns | 7 |
health surveillance | 7 |
common symptoms | 7 |
crucial role | 7 |
tweet contains | 7 |
negative matrix | 7 |
better understanding | 7 |
detection algorithms | 7 |
digital communications | 7 |
mueller investigation | 7 |
twitter vocabulary | 7 |
null model | 7 |
wing community | 7 |
save lives | 7 |
matrix factorization | 7 |
note springer | 7 |
sentiment monitoring | 7 |
degree sequence | 7 |
increasing number | 7 |
small news | 7 |
positive users | 7 |
recurrent neural | 7 |
every user | 7 |
top ten | 7 |
media outlets | 7 |
grained linguistic | 7 |
dependency parsing | 7 |
science research | 7 |
twitter discussion | 7 |
also provides | 7 |
acute respiratory | 7 |
social life | 7 |
single tweet | 7 |
application programming | 7 |
data technologies | 7 |
tweets refuting | 7 |
sharing information | 7 |
classification problem | 7 |
section iv | 7 |
empirical studies | 7 |
monitoring public | 7 |
tweet data | 7 |
springer nature | 7 |
jurisdictional claims | 7 |
per minute | 7 |
financial support | 7 |
media systems | 7 |
second step | 7 |
media mining | 7 |
human perception | 7 |
discussion around | 7 |
retweet cascade | 7 |
remains neutral | 7 |
control variables | 7 |
chinese government | 7 |
frequent words | 7 |
flu trends | 7 |
document frequency | 7 |
word vectors | 7 |
lockdown measures | 7 |
eigenvector centrality | 7 |
different communities | 7 |
graph processing | 7 |
severe acute | 7 |
online platforms | 7 |
malicious content | 7 |
give us | 7 |
missile test | 7 |
disease ontology | 7 |
political sub | 7 |
social sensors | 7 |
positive rate | 7 |
hybrid model | 7 |
news item | 7 |
probability distribution | 7 |
media discourse | 7 |
institutional affiliations | 7 |
online political | 7 |
motivation theory | 7 |
first three | 7 |
section describes | 7 |
cwts classification | 7 |
next step | 7 |
people use | 7 |
table provides | 7 |
many twitter | 7 |
using natural | 7 |
attention received | 7 |
individual tweets | 7 |
reported cases | 7 |
many countries | 7 |
world events | 7 |
recent research | 7 |
decision tree | 7 |
important information | 7 |
statistical analysis | 7 |
results obtained | 7 |
keyword analysis | 7 |
named entity | 7 |
will discuss | 7 |
top words | 7 |
based social | 7 |
death nls | 7 |
key value | 7 |
facebook use | 7 |
provide information | 7 |
media sources | 7 |
team brand | 7 |
high abuse | 7 |
surveillance systems | 7 |
false political | 7 |
spatial uncertainty | 7 |
first time | 7 |
research center | 7 |
collected twitter | 7 |
infected cases | 7 |
johns hopkins | 7 |
coronavirus twitter | 7 |
fake accounts | 7 |
relationships among | 7 |
general election | 7 |
detailed information | 7 |
data using | 7 |
creative commons | 7 |
classification using | 7 |
true information | 7 |
series analysis | 7 |
significant predictor | 7 |
several times | 7 |
hashtags related | 7 |
step classification | 7 |
relevant tweets | 7 |
input data | 7 |
analysis tasks | 7 |
top hashtags | 7 |
word vec | 7 |
attention mechanism | 7 |
also included | 7 |
spanish newspapers | 7 |
tagged tweets | 7 |
epidemic outbreak | 7 |
like twitter | 7 |
information spread | 7 |
non trivial | 7 |
may increase | 7 |
search engine | 7 |
media activity | 7 |
medical conferences | 7 |
based brand | 7 |
health problems | 7 |
recent studies | 7 |
real network | 7 |
distancing rules | 7 |
search terms | 7 |
allowed us | 7 |
four main | 7 |
protection motivation | 7 |
nature remains | 7 |
journal club | 7 |
terms related | 7 |
active twitter | 7 |
media posts | 7 |
discussion topics | 6 |
data shows | 6 |
different phases | 6 |
weakly supervised | 6 |
information provided | 6 |
will return | 6 |
global graph | 6 |
existing studies | 6 |
depressive symptoms | 6 |
semantic features | 6 |
influenza epidemic | 6 |
fuzzy set | 6 |
control group | 6 |
mistakenly released | 6 |
decision making | 6 |
weaker relationship | 6 |
collective action | 6 |
analyzing twitter | 6 |
ebola outbreak | 6 |
data model | 6 |
median time | 6 |
use cases | 6 |
network dynamics | 6 |
historical tweets | 6 |
best performing | 6 |
strongly subjective | 6 |
news stories | 6 |
social issues | 6 |
interestingly enough | 6 |
weakly subjective | 6 |
urgent endoscopy | 6 |
mlb attendance | 6 |
harmful media | 6 |
detection methods | 6 |
trending topics | 6 |
web page | 6 |
early february | 6 |
learning techniques | 6 |
specific hashtags | 6 |
conservative party | 6 |
public information | 6 |
context extraction | 6 |
concern timeline | 6 |
opening remarks | 6 |
bursty phrases | 6 |
york city | 6 |
user data | 6 |
german speaking | 6 |
identify hot | 6 |
proposed algorithm | 6 |
current findings | 6 |
chinese social | 6 |
class imbalance | 6 |
social isolation | 6 |
tweets based | 6 |
british mps | 6 |
epidemiological modeling | 6 |
google news | 6 |
i know | 6 |
search api | 6 |
health informatics | 6 |
extraction patterns | 6 |
geographic regions | 6 |
high dimensional | 6 |
ebola virus | 6 |
semantic web | 6 |
prior exposure | 6 |
research studies | 6 |
six main | 6 |
facebook users | 6 |
irrelevant tweets | 6 |
data stream | 6 |
baseball attendance | 6 |
daily death | 6 |
coronavirus topic | 6 |
posted june | 6 |
story attributed | 6 |
social impact | 6 |
word pair | 6 |
important issue | 6 |
previous subsection | 6 |
paper presents | 6 |
deliberate sharing | 6 |
gated recurrent | 6 |
configuration model | 6 |
big social | 6 |
section provides | 6 |
three symptoms | 6 |
test surge | 6 |
large amount | 6 |
tab delimited | 6 |
temporal dynamics | 6 |
story online | 6 |
news items | 6 |
health organizations | 6 |
spatial distribution | 6 |
intensive care | 6 |
effect sizes | 6 |
john hopkins | 6 |
educated people | 6 |
randomly sampled | 6 |
secondary data | 6 |
dark orange | 6 |
news outlets | 6 |
user posts | 6 |
hate diffusion | 6 |
media infodemic | 6 |
semantic role | 6 |
ground truth | 6 |
political situation | 6 |
make sense | 6 |
instagram users | 6 |
lower conscientiousness | 6 |
also consider | 6 |
thematic maps | 6 |
lower age | 6 |
dictionary learning | 6 |
missing data | 6 |
ecology framework | 6 |
diffusion models | 6 |
initial tweet | 6 |
media briefing | 6 |
predictive models | 6 |
knowledge discovery | 6 |
high precision | 6 |
twitter journal | 6 |
previous works | 6 |
crime reduction | 6 |
times higher | 6 |
positive tweets | 6 |
undirected version | 6 |
global crisis | 6 |
perceptual screen | 6 |
positive cases | 6 |
given time | 6 |
relevant data | 6 |
zika virus | 6 |
blue subcommunity | 6 |
word pairs | 6 |
negative words | 6 |
role labeling | 6 |
step sentiment | 6 |
rated likelihood | 6 |
unsupervised machine | 6 |
traditional news | 6 |
study shows | 6 |
public policies | 6 |
dimensionality reduction | 6 |
towards uk | 6 |
foreign residents | 6 |
data used | 6 |
folk devils | 6 |
citation counts | 6 |
media content | 6 |
lda models | 6 |
see methods | 6 |
information may | 6 |
hopkins university | 6 |
manually reviewed | 6 |
scale covid | 6 |
vaccine sentiment | 6 |
social processes | 6 |
mutual information | 6 |
chinese propaganda | 6 |
take care | 6 |
potentially harmful | 6 |
exogenous attention | 6 |
classify tweets | 6 |
eid outbreak | 6 |
two newspapers | 6 |
detecting depression | 6 |
user profile | 6 |
white house | 6 |
coronavirus resource | 6 |
emotional response | 6 |
health emergencies | 6 |
toward mps | 6 |
timely manner | 6 |
toward uk | 6 |
user id | 6 |
geo version | 6 |
intensive attention | 6 |
situational awareness | 6 |
disease activity | 6 |
computational intelligence | 6 |
network sites | 6 |
flu pandemic | 6 |
meeting hashtag | 6 |
series data | 6 |
liwc dictionary | 6 |
crisis events | 6 |
first week | 6 |
tweets sent | 6 |
national security | 6 |
th march | 6 |
regression coefficients | 6 |
information sources | 6 |
disaster resilience | 6 |
clinical settings | 6 |
data show | 6 |
subcommunity contains | 6 |
linear model | 6 |
negative binomial | 6 |
detection model | 6 |
twitter corpora | 6 |
psychophysical laws | 6 |
topic coherence | 6 |
based null | 6 |
related messages | 6 |
based model | 6 |
better performance | 6 |
general crime | 6 |
annotated datasets | 6 |
total correlation | 6 |
results showed | 6 |
online information | 6 |
specific data | 6 |
main text | 6 |
pandemic date | 6 |
related work | 6 |
linguistic features | 6 |
disaster relief | 6 |
table ii | 6 |
offensive output | 6 |
recent work | 6 |
study using | 6 |
word set | 6 |
south america | 6 |
slightly different | 6 |
retweet dynamics | 6 |
stress words | 6 |
relevant nodes | 6 |
epidemics using | 6 |
health agencies | 6 |
another study | 6 |
help us | 6 |
location spoofing | 6 |
general elections | 6 |
american society | 6 |
feature extraction | 6 |
main subject | 6 |
dominic cummings | 6 |
information shared | 6 |
negative emotions | 6 |
platforms like | 6 |
entity recognition | 6 |
will help | 6 |
current flow | 6 |
key topics | 6 |
predictive analytics | 6 |
will get | 6 |
target audience | 6 |
removed cases | 6 |
media communication | 6 |
tweets contained | 6 |
different hashtags | 6 |
covid tweets | 6 |
untrue political | 6 |
equity model | 6 |
digital traces | 6 |
opposition parties | 5 |
used twitter | 5 |
test dataset | 5 |
digital age | 5 |
disaster communication | 5 |
matt hancock | 5 |
human annotated | 5 |
one possible | 5 |
communication strategies | 5 |
predictor variable | 5 |
world population | 5 |
annual meeting | 5 |
deliberate historical | 5 |
network data | 5 |
political disinformation | 5 |
two layers | 5 |
near real | 5 |
previous method | 5 |
crime types | 5 |
personal reports | 5 |
analyzing tweets | 5 |
mainly composed | 5 |
observed pattern | 5 |
level features | 5 |
research articles | 5 |
one hand | 5 |
com jacklopresti | 5 |
rss feeds | 5 |
observational study | 5 |
different topics | 5 |
detecting public | 5 |
later found | 5 |
random forest | 5 |
february th | 5 |
language toolkit | 5 |
detection method | 5 |
also important | 5 |
tweet received | 5 |
will need | 5 |
three periods | 5 |
people also | 5 |
news peaks | 5 |
news agencies | 5 |
tweets received | 5 |
information fusion | 5 |
test whether | 5 |
reputable sources | 5 |
also called | 5 |
online debate | 5 |
results presented | 5 |
user base | 5 |
data science | 5 |
among users | 5 |
tweets without | 5 |
health facilities | 5 |
critical equipment | 5 |
unclear whether | 5 |
current covid | 5 |
labeling method | 5 |
republican parties | 5 |
staying home | 5 |
presidential campaign | 5 |
multiple sources | 5 |
brexit coverage | 5 |
death cases | 5 |
young adults | 5 |
retweeting times | 5 |
community resilience | 5 |
python package | 5 |
public safety | 5 |
feature representation | 5 |
people reporting | 5 |
multiple studies | 5 |
two attributes | 5 |
demographic characteristics | 5 |
late may | 5 |
disease detection | 5 |
distribution patterns | 5 |
timeline trend | 5 |
global public | 5 |
south alabama | 5 |
recently proposed | 5 |
german language | 5 |
sentiment scores | 5 |
sports marketing | 5 |
considerable amount | 5 |
phq level | 5 |
three times | 5 |
using two | 5 |
subjective clues | 5 |
promoting tweets | 5 |
online communication | 5 |
organic diffusion | 5 |
data privacy | 5 |
average number | 5 |
hateful behavior | 5 |
news sites | 5 |
crisis stage | 5 |
covid related | 5 |
across research | 5 |
decision makers | 5 |
users tend | 5 |
first research | 5 |
basilisk algorithm | 5 |
profanity word | 5 |
political agenda | 5 |
user mentions | 5 |
user interactions | 5 |
papers published | 5 |
model using | 5 |
around million | 5 |
tweeting practices | 5 |
media using | 5 |
physical magnitude | 5 |
twitter datasets | 5 |
prediction task | 5 |
specific words | 5 |
different frames | 5 |
existing literature | 5 |
political memes | 5 |
manual grouping | 5 |
network structure | 5 |
one week | 5 |
news feed | 5 |
terrorist attacks | 5 |
concerns regarding | 5 |
sentiment timeline | 5 |
distributed storage | 5 |
events related | 5 |
topic embedding | 5 |
coherence measure | 5 |
correlation explanation | 5 |
web sites | 5 |
mechanical turk | 5 |
specific features | 5 |
biggest communities | 5 |
compromised accounts | 5 |
boolean terms | 5 |
twitter based | 5 |
public relations | 5 |
complex networks | 5 |
hate detection | 5 |
articles published | 5 |
sparse matrix | 5 |
yemen cholera | 5 |
sentiment classifier | 5 |
using machine | 5 |
mental disorders | 5 |
across multiple | 5 |
study found | 5 |
large amounts | 5 |
political communication | 5 |
search results | 5 |
stress level | 5 |
knowledge dissemination | 5 |
altmetric event | 5 |
trending topic | 5 |
latent features | 5 |
two frames | 5 |
intensity values | 5 |
non verified | 5 |
cognitive systems | 5 |
social relations | 5 |
whether tweets | 5 |
clustering algorithms | 5 |
information processing | 5 |
liberal democrats | 5 |
trauma surgery | 5 |
based models | 5 |
online media | 5 |
search engines | 5 |
shared material | 5 |
current study | 5 |
web mining | 5 |
vector space | 5 |
stronger relationship | 5 |
description see | 5 |
public responses | 5 |
user timeline | 5 |
epidemiological data | 5 |
publication level | 5 |
clinical studies | 5 |
words across | 5 |
public attention | 5 |
topological properties | 5 |
specific tweets | 5 |
using data | 5 |
feature vector | 5 |
retweeter prediction | 5 |
processing techniques | 5 |
public opinions | 5 |
mining social | 5 |
better results | 5 |
moving average | 5 |
scientific research | 5 |
social consequences | 5 |
spectral clustering | 5 |
studies based | 5 |
related news | 5 |
share false | 5 |
news content | 5 |
daily life | 5 |
like characteristics | 5 |
information available | 5 |
coronavirus outbreak | 5 |
team performance | 5 |
quantitative performance | 5 |
possible explanation | 5 |
data quality | 5 |
temporal trends | 5 |
crimson hexagon | 5 |
high number | 5 |
receiving abuse | 5 |
often used | 5 |
tom buchanan | 5 |
health communication | 5 |
least symptom | 5 |
national daily | 5 |
higher level | 5 |
speaking twitter | 5 |
crime data | 5 |
potential retweeters | 5 |
revenue sharing | 5 |
many different | 5 |
specific topics | 5 |
mining twitter | 5 |
hateful content | 5 |
semantic space | 5 |
participant demographics | 5 |
research interests | 5 |
table lists | 5 |
validated projection | 5 |
media space | 5 |
health sciences | 5 |
studies related | 5 |
data volume | 5 |
help understand | 5 |
popular topics | 5 |
wikipedia citations | 5 |
scale social | 5 |
risk information | 5 |
place managers | 5 |
reddit mentions | 5 |
world series | 5 |
several limitations | 5 |
increasing use | 5 |
hateful contents | 5 |
twitter analysis | 5 |
one day | 5 |
new method | 5 |
formal outbreak | 5 |
one user | 5 |
gender difference | 5 |
disaster events | 5 |
time i | 5 |
relevant terms | 5 |
fast unfolding | 5 |
march st | 5 |
testing positive | 5 |
th may | 5 |
center left | 5 |
increased cases | 5 |
candidate word | 5 |
small number | 5 |
identifying hot | 5 |
best results | 5 |
viral marketing | 5 |
korea missile | 5 |
show different | 5 |
dynamic setting | 5 |
trained word | 5 |
effect size | 5 |
diffusion process | 5 |
twitter information | 5 |
identified hot | 5 |
information flows | 5 |
digitally mediated | 5 |
people reported | 5 |
high coverage | 5 |
predicting depression | 5 |
enforcement agencies | 5 |
media metrics | 5 |
three stimuli | 5 |
using topic | 5 |
coronavirus pandemic | 5 |
strong correlation | 5 |
sharing false | 5 |
regression methods | 5 |
noisy data | 5 |
two ways | 5 |
reputable news | 5 |
behavioral analysis | 5 |
age group | 5 |
psychologically meaningful | 5 |
research trends | 5 |
dark red | 5 |
specific disease | 5 |
apache hadoop | 5 |
black lives | 5 |
web service | 5 |
working hard | 5 |
establish whether | 5 |
nhkworld news | 5 |
wide number | 5 |
information extracted | 5 |
fraudulent accounts | 5 |
data twitter | 5 |
also collected | 5 |
model performance | 5 |
i used | 5 |
higher neuroticism | 5 |
much higher | 5 |
million unique | 5 |
divert attention | 5 |
temporal distribution | 5 |
assess disease | 5 |
clinical science | 5 |
study aims | 5 |
different hashtag | 5 |
linguistic score | 5 |
responsible agents | 5 |
final dataset | 5 |
sentiment trends | 5 |
data streams | 5 |
new cases | 5 |
covid pandemic | 5 |
information conflict | 5 |
bayes classifier | 5 |
lives matter | 5 |
foreign policy | 5 |
find words | 5 |
final phq | 5 |
collecting data | 5 |
community groups | 5 |
bipartite networks | 5 |
two major | 5 |
territorial police | 5 |
twitter handles | 5 |
societal impact | 5 |
new research | 5 |
based applications | 5 |
endoscopic procedures | 5 |
statistical significance | 5 |
surveillance using | 5 |
negative sentiments | 5 |
different contexts | 5 |
regular expressions | 5 |
tweet id | 5 |
violet red | 5 |
lock downs | 5 |
resulting network | 5 |
first stage | 5 |
training datasets | 5 |
word list | 5 |
healthmap project | 5 |
special characters | 5 |
jacklopresti status | 5 |
model outperforms | 5 |
qualitative sample | 5 |
dbpedia spotlight | 5 |
blue line | 5 |
information spreading | 5 |
agreeable people | 5 |
will allow | 5 |
verified accounts | 5 |
upper endoscopy | 5 |
home save | 5 |
stop word | 5 |
phq stress | 5 |
topics discussed | 5 |
detect depression | 5 |
recent tweets | 5 |
response rate | 5 |
body ache | 5 |
two independent | 5 |
several tweets | 5 |
section presents | 5 |
strong floor | 5 |
george floyd | 5 |
covid twitter | 5 |
new data | 5 |
notation description | 5 |
calculated using | 5 |
forward layer | 5 |
generally speaking | 5 |
tweets originating | 5 |
make use | 5 |
computational propaganda | 5 |
twitter platform | 5 |
word pool | 5 |
higher abuse | 5 |
computer science | 5 |
extract latent | 4 |
uniform manifold | 4 |
centrality measures | 4 |
spanish government | 4 |
topic groups | 4 |
death toll | 4 |
st century | 4 |
sentiment quantification | 4 |
liminal hotspot | 4 |
clean data | 4 |
take place | 4 |
relevant knowledge | 4 |
manually annotated | 4 |
relevant features | 4 |
different parties | 4 |
collect tweets | 4 |
second stage | 4 |
people may | 4 |
twitter will | 4 |
weighted average | 4 |
topics using | 4 |
planned analysis | 4 |
classification algorithm | 4 |
first one | 4 |
richard burgon | 4 |
significant variation | 4 |
high phq | 4 |
panic production | 4 |
data fusion | 4 |
activity regarding | 4 |
mental stress | 4 |
dom cummings | 4 |
word representations | 4 |
media usage | 4 |
th june | 4 |
utc format | 4 |
informal outbreak | 4 |
general location | 4 |
social sensor | 4 |
seasonal offensive | 4 |
disease monitoring | 4 |
younger people | 4 |
epidemic model | 4 |
twitter abuse | 4 |
subjective words | 4 |
large numbers | 4 |
using weakly | 4 |
tweet body | 4 |
cyberbullying detection | 4 |
times larger | 4 |
twitter search | 4 |
random sample | 4 |
time difference | 4 |
emergency response | 4 |
will continue | 4 |
infection rate | 4 |
coherence scores | 4 |
twitter website | 4 |
difficult task | 4 |
police officers | 4 |
essential goods | 4 |
visual features | 4 |
three time | 4 |
intelligence network | 4 |
news updates | 4 |
false positive | 4 |
timeline chart | 4 |
spearman correlation | 4 |
meeting attendance | 4 |
muslim community | 4 |
tweet phq | 4 |
works well | 4 |
research evaluation | 4 |
global disease | 4 |
comparative study | 4 |
analysis shows | 4 |
disease prevention | 4 |
evaluation metrics | 4 |
users worldwide | 4 |
similar results | 4 |
engagement scores | 4 |
factual content | 4 |
detect depressed | 4 |
public life | 4 |
group polarization | 4 |
least three | 4 |
wing oriented | 4 |
information cascade | 4 |
useful knowledge | 4 |
critical care | 4 |
point detection | 4 |
take advantage | 4 |
prediction model | 4 |
keir starmer | 4 |
material known | 4 |
one example | 4 |
hemodynamic instability | 4 |
twitter application | 4 |
retweeting activity | 4 |
various altmetric | 4 |
studies conducted | 4 |
users expressed | 4 |
information exchange | 4 |
target sample | 4 |
epidemic analysis | 4 |
action word | 4 |
topic distribution | 4 |
clinical characteristics | 4 |
blog posts | 4 |
chinese information | 4 |
distributed systems | 4 |
data frameworks | 4 |
local communities | 4 |
susceptible nodes | 4 |
political debate | 4 |
many news | 4 |
specific social | 4 |
statistical techniques | 4 |
toward british | 4 |
national covid | 4 |
shared one | 4 |
last column | 4 |
presidential elections | 4 |
account creation | 4 |
engagement score | 4 |
main dependent | 4 |
lagged variables | 4 |
consensus information | 4 |
tweets shared | 4 |
batch size | 4 |
top influencers | 4 |
conversation around | 4 |
current situation | 4 |
tracking social | 4 |
collecting twitter | 4 |
haiti cholera | 4 |
significant negative | 4 |
marketing campaigns | 4 |
disease alert | 4 |
coverage across | 4 |
one order | 4 |
aast meeting | 4 |
twitter developers | 4 |
language understanding | 4 |
different areas | 4 |
right time | 4 |
political communities | 4 |
lombardy region | 4 |
less abusive | 4 |
test hypotheses | 4 |
authors propose | 4 |
biocaster ontology | 4 |
world leaders | 4 |
eating disorder | 4 |
max function | 4 |
following research | 4 |
prime recommendations | 4 |
whether messages | 4 |
two pairs | 4 |
data repository | 4 |
news stop | 4 |
public may | 4 |
positive sentiment | 4 |
three events | 4 |
social computing | 4 |
twitter discussions | 4 |
social terms | 4 |
binomial distribution | 4 |
symptoms reported | 4 |
related replies | 4 |
low recall | 4 |
mentioned previously | 4 |
wos papers | 4 |
review board | 4 |
traditional media | 4 |
word representation | 4 |
social attention | 4 |
moc peaks | 4 |
strongest predictor | 4 |
three general | 4 |
extract features | 4 |
economic modeling | 4 |
also shows | 4 |
russian information | 4 |
web pages | 4 |
western europe | 4 |
selected publications | 4 |
allowing users | 4 |
many states | 4 |
academic promotion | 4 |
root tweet | 4 |
media may | 4 |
hundred thousand | 4 |
individual differences | 4 |
analysis system | 4 |
conspiracy narrative | 4 |
cognitive ability | 4 |
every minute | 4 |
recurrent unit | 4 |
certain level | 4 |
epidemic crisis | 4 |
social interaction | 4 |
research response | 4 |
patients presenting | 4 |
different aspects | 4 |
comparative assessment | 4 |
related phrases | 4 |
related conspiracy | 4 |
research purposes | 4 |
two distinct | 4 |
uniquely identifying | 4 |
incubation period | 4 |
large data | 4 |
american association | 4 |
observation period | 4 |
change point | 4 |
graph shows | 4 |
directed graph | 4 |
cascade prediction | 4 |
followers reflect | 4 |
science publications | 4 |
streaming data | 4 |
us sample | 4 |
health concern | 4 |
moc timeline | 4 |
provides two | 4 |
assessment score | 4 |
arg pr | 4 |
stage least | 4 |
completed online | 4 |
spatiotemporal patterns | 4 |
tweet sentiment | 4 |
identify relevant | 4 |
economic behavior | 4 |
death statistics | 4 |
three columns | 4 |
assessment method | 4 |
mps via | 4 |
users also | 4 |
conceptual framework | 4 |
geographic distribution | 4 |
relevant keywords | 4 |
document clustering | 4 |
alert map | 4 |
social graph | 4 |
health services | 4 |
political information | 4 |
country i | 4 |
feature set | 4 |
potentially threatening | 4 |
cybernetic feedback | 4 |
country basis | 4 |
various features | 4 |
boosting immunity | 4 |
using multi | 4 |
runs per | 4 |
model explained | 4 |
flow betweenness | 4 |
data generated | 4 |
los angeles | 4 |
future disaster | 4 |
twitter allows | 4 |
group users | 4 |
tweet pharse | 4 |
matthancock status | 4 |
web search | 4 |
strongly correlated | 4 |
politics frame | 4 |
general diseases | 4 |
word analysis | 4 |
current tweet | 4 |
substantive sections | 4 |
using logistic | 4 |
information within | 4 |
infection tweets | 4 |
highest correlation | 4 |
quarantine zone | 4 |
simultaneously test | 4 |
per game | 4 |
naturalistic setting | 4 |
possible future | 4 |
done using | 4 |
kivu ebola | 4 |
next stage | 4 |
tweets corresponding | 4 |
twitter predicting | 4 |
upper gastrointestinal | 4 |
tell us | 4 |
network modeling | 4 |
digital technologies | 4 |
json objects | 4 |
information systems | 4 |
extracted features | 4 |
generated data | 4 |
death rates | 4 |
data preprocessing | 4 |
chronological order | 4 |
participant attitudes | 4 |
sharing behaviour | 4 |
altmetric indicators | 4 |
first years | 4 |
hashtag cloud | 4 |
hierarchical clustering | 4 |
based topic | 4 |
supervised topic | 4 |
learning interface | 4 |
automated text | 4 |
covid monitor | 4 |
positive correlation | 4 |
examined using | 4 |
exogenous signal | 4 |
national government | 4 |
news online | 4 |
processing algorithms | 4 |
decreased significantly | 4 |
covid retweeting | 4 |
present results | 4 |
chatter dataset | 4 |
acceptable reliability | 4 |
collective emotions | 4 |
media companies | 4 |
publication years | 4 |
linguistic variables | 4 |
partially mediates | 4 |
cultural studies | 4 |
uk law | 4 |
family members | 4 |
suspicious posts | 4 |
emerging diseases | 4 |
rapid spread | 4 |
three dimensions | 4 |
online communities | 4 |
hashtags used | 4 |
unidad ciudadana | 4 |
related topics | 4 |
unknowingly shared | 4 |
apache spark | 4 |
san francisco | 4 |
classification task | 4 |
weighted edges | 4 |
published online | 4 |
northern italy | 4 |
suspicious accounts | 4 |
unverified accounts | 4 |
tweets matching | 4 |
shifting users | 4 |
connection strength | 4 |
timeline trends | 4 |
election campaign | 4 |
highly skewed | 4 |
rsn contracts | 4 |
generated using | 4 |
particular place | 4 |
manifold approximation | 4 |
positive negative | 4 |
arabic content | 4 |
distributed file | 4 |
matter protests | 4 |
uk police | 4 |
academic research | 4 |
short time | 4 |
opposite direction | 4 |
team offense | 4 |
analyzing social | 4 |
second period | 4 |
time segments | 4 |
pandemic intensifies | 4 |
official twitter | 4 |
semantic information | 4 |
response patterns | 4 |
another example | 4 |
health agency | 4 |
tweet corpus | 4 |
google flu | 4 |
competing interests | 4 |
real people | 4 |
strongly negative | 4 |
occurrence network | 4 |
level predictors | 4 |
top five | 4 |
model based | 4 |
dynamic settings | 4 |
equation modeling | 4 |
fake transitions | 4 |
majority tweet | 4 |
vector machines | 4 |
two data | 4 |
timeline posts | 4 |
time interval | 4 |
series wins | 4 |
physical distancing | 4 |
user account | 4 |
south american | 4 |
new technologies | 4 |
abuse towards | 4 |
will report | 4 |
economic impact | 4 |
emergency alert | 4 |
overall presence | 4 |
will focus | 4 |
business intelligence | 4 |
posts per | 4 |
topic analysis | 4 |
i will | 4 |
influenza surveillance | 4 |
large groups | 4 |
exploratory data | 4 |
last one | 4 |
political preference | 4 |
among gastroenterologists | 4 |
search queries | 4 |
directed network | 4 |
persuasive topics | 4 |
table iv | 4 |
altmetric attention | 4 |
wide range | 4 |
popular social | 4 |
systematic literature | 4 |
networks using | 4 |
significant predictors | 4 |
police use | 4 |
global pandemics | 4 |
health intelligence | 4 |
word vector | 4 |
user profiles | 4 |
also includes | 4 |
tools used | 4 |
six different | 4 |
using word | 4 |
database called | 4 |
logistic regressions | 4 |
user behavior | 4 |
crisis response | 4 |
modus operandi | 4 |
daily distribution | 4 |
main contributions | 4 |
study focuses | 4 |
social restrictions | 4 |
research works | 4 |
means clustering | 4 |
north american | 4 |
proposed corexq | 4 |
rapid research | 4 |
early stage | 4 |
data screening | 4 |
visual abstracts | 4 |
hurdle model | 4 |
semantically meaningful | 4 |
unannotated corpus | 4 |
personal infection | 4 |
value creation | 4 |
international collaboration | 4 |
two groups | 4 |
louvain method | 4 |
three primary | 4 |
million population | 4 |
results reported | 4 |
raw tweets | 4 |
uk government | 4 |
media will | 4 |
open data | 4 |
also find | 4 |
much lower | 4 |
survey data | 4 |
political affiliation | 4 |
com matthancock | 4 |
shared false | 4 |
large datasets | 4 |
valuable data | 4 |
subjective terms | 4 |
different issues | 4 |
mr johnson | 4 |
television contracts | 4 |
among individuals | 4 |
first half | 4 |
uk sample | 4 |
current context | 4 |
either male | 4 |
academic medicine | 4 |
graph machine | 4 |
main italian | 4 |
structural equation | 4 |
json format | 4 |
attendance increases | 4 |
research papers | 4 |
qualitatively understand | 4 |
influential accounts | 4 |
privacy issues | 4 |
national lockdown | 4 |
using facebook | 4 |
provide insight | 4 |
high frequency | 4 |
ten posts | 4 |
attend games | 4 |
disaster preparedness | 4 |
media analysis | 4 |
new zealand | 4 |
detection system | 4 |
unique opportunity | 4 |
ssh publications | 4 |
reply tweet | 4 |
social practices | 4 |
eight news | 4 |
preferred topics | 4 |
events detected | 4 |
examine diffusion | 4 |
gastroenterologists regarding | 4 |
following sections | 4 |
study twitter | 4 |
major concerns | 4 |
twitter chatter | 4 |
contextual information | 4 |
learning may | 4 |
thousand population | 4 |
gastrointestinal bleeding | 4 |
analyse data | 4 |
contain abusive | 4 |
section reviews | 4 |
deep state | 4 |
retweet networks | 4 |
abusive responses | 4 |
embedding models | 4 |
natural hazards | 4 |
death score | 4 |
media information | 4 |
hashtags mentioned | 4 |
mining toolkit | 4 |
brand loyalty | 4 |
usage analysis | 4 |
health professions | 4 |
feature importance | 4 |
using commonly | 4 |
study revealed | 4 |
absolutely right | 4 |
batch mode | 4 |
marketing measures | 4 |
suspicious profiles | 4 |
user activity | 4 |
travel north | 4 |
economic disruption | 4 |
sports teams | 4 |
received abusive | 4 |
projection procedure | 4 |
political figureheads | 4 |
authors found | 4 |
based clustering | 4 |
twitter social | 4 |
system continuously | 4 |
sans frontieres | 4 |
independent ols | 4 |
clinical oncology | 4 |
depressed user | 4 |
depth analysis | 4 |
data related | 4 |
count data | 4 |
healthcare providers | 4 |
health specialists | 4 |
public officials | 4 |
suspended accounts | 4 |
empirical analysis | 4 |
phq category | 4 |
online conversations | 4 |
valuable insight | 4 |
center right | 4 |
hate tweets | 4 |
important news | 4 |
information associated | 4 |
key terms | 4 |
video comments | 4 |
feature fusion | 4 |
surrounding covid | 4 |
negative emoticon | 4 |
proposed model | 4 |
data point | 4 |
publications across | 4 |
linguistic inquiry | 4 |
general awareness | 4 |
convolutional neural | 4 |
floor effect | 4 |
users express | 4 |
across five | 4 |
change across | 4 |
ebola epidemic | 4 |
higher numbers | 4 |
pse publications | 4 |
cascade length | 4 |
thousand tweets | 4 |
also identified | 4 |
reliable data | 4 |
recent advances | 4 |
collection process | 4 |
used hashtags | 4 |
unprecedented times | 4 |
official sources | 4 |
tweet using | 4 |
tweets expressing | 4 |
time disease | 4 |
labour party | 4 |
spatiotemporal scale | 4 |
node degree | 4 |
media communications | 4 |
systems based | 4 |
hateful tweet | 4 |
highly contagious | 4 |
chat platform | 4 |
syndromic surveillance | 4 |
persuasive arguments | 4 |
learning algorithm | 4 |
television viewership | 4 |
topic classification | 4 |
many parents | 4 |
information source | 4 |
baseline methods | 4 |
urgent scenarios | 4 |
public risk | 4 |
measured using | 4 |
research findings | 4 |
white women | 4 |
disinformation messages | 4 |
seven days | 4 |
twitter json | 4 |
buying group | 4 |
another one | 4 |
used data | 4 |
time using | 4 |
biomedical text | 4 |
latent topics | 4 |
useful information | 4 |
individual tweet | 4 |
louvain algorithm | 4 |
health monitoring | 4 |
first public | 4 |
global health | 4 |
first place | 4 |
previous results | 4 |
target audiences | 4 |
institutional review | 4 |
problems related | 4 |
network features | 4 |
digital data | 4 |
epidemiological indicators | 4 |
best performance | 4 |
manually labeled | 4 |
worldwide data | 4 |
trending covid | 4 |
english words | 4 |
seed labels | 4 |
see eq | 4 |
municipal government | 4 |
ongoing covid | 4 |
association rules | 4 |
table summarizes | 4 |
available tweets | 4 |
far greater | 4 |
concerned covid | 4 |
classification methods | 4 |
prior research | 4 |
quick search | 4 |
mediation results | 4 |
respondents indicated | 4 |
media tweet | 4 |
patient privacy | 4 |
analytics tools | 4 |
resource center | 4 |
sentiment strength | 4 |
independent cascade | 4 |
topics identified | 4 |
interactive web | 4 |
linguistic word | 4 |
search function | 4 |
health professionals | 4 |
tv programs | 4 |
given inclusion | 4 |
may use | 4 |
respondents thought | 4 |
brief summary | 4 |
open scientific | 4 |
different domains | 4 |
cosine similarity | 4 |
wide variety | 4 |
daily deaths | 4 |
similar trend | 4 |
support system | 4 |
specific content | 4 |
semantic lexicons | 4 |
important role | 4 |
online misinformation | 4 |
centered around | 4 |
scalable seeded | 4 |
events may | 4 |
traditional data | 4 |
human trafficking | 4 |
generated topics | 4 |
classification approach | 4 |
italian prime | 4 |
consensus among | 4 |
leibler divergence | 4 |
model parameters | 4 |
shed light | 4 |
monitor austria | 4 |
regarding covid | 4 |
three days | 4 |
story source | 4 |
three stories | 4 |
news article | 4 |
computational complexity | 4 |
migration policing | 4 |
manual coding | 4 |
study news | 4 |
twitter hashtags | 4 |
threatening media | 4 |
crime specific | 4 |
moderately correlated | 4 |
five main | 4 |
instructional information | 4 |
retweeting time | 4 |
first author | 4 |
slate blue | 4 |
political orientations | 4 |
police force | 4 |
strongly positive | 4 |
cnn network | 4 |
document corpus | 4 |
different levels | 4 |
phq label | 4 |
epidemic events | 4 |
research paper | 4 |
tweet type | 4 |
abuse received | 4 |
linguistic scores | 4 |
makes sense | 4 |
high risk | 4 |
coronavirus conspiracy | 4 |
allow us | 4 |
situational information | 4 |
critical public | 4 |
commons attribution | 4 |
democratic party | 4 |
human activities | 4 |
pos tags | 4 |
actual case | 4 |
warning signs | 3 |
mlb team | 3 |
including social | 3 |
conference material | 3 |
behavior via | 3 |
frequency inverse | 3 |
among people | 3 |
examples include | 3 |
analysis will | 3 |
specific keywords | 3 |
information system | 3 |
internet research | 3 |
trust authorities | 3 |
may contain | 3 |
various tasks | 3 |
british political | 3 |
trained model | 3 |
bandwidth usage | 3 |
crime type | 3 |
similar trends | 3 |
retweeting messages | 3 |
pause censorship | 3 |
analytics approach | 3 |
epub ahead | 3 |
negatively correlated | 3 |
common covid | 3 |
spatial context | 3 |
network obtained | 3 |
misinformation epidemic | 3 |
following analysis | 3 |
gastroenterologists agreed | 3 |
processing methods | 3 |
methodology exactly | 3 |
time twitter | 3 |
national linguistic | 3 |
risk perceptions | 3 |
words present | 3 |
partido justicialista | 3 |
certain political | 3 |
convolution layer | 3 |
numbing effect | 3 |
academic institutions | 3 |
also refer | 3 |
relevant news | 3 |
direct relationship | 3 |
related knowledge | 3 |
chicago cubs | 3 |
various types | 3 |
research model | 3 |
print media | 3 |
distancing policies | 3 |
spread research | 3 |
independent variables | 3 |
vary significantly | 3 |
web interface | 3 |
politicians receiving | 3 |
rich set | 3 |
publications mentioned | 3 |
distributed across | 3 |
conference materials | 3 |
among various | 3 |
decision trees | 3 |
large dataset | 3 |
across publications | 3 |
binary segmentation | 3 |
public support | 3 |
repeated exposure | 3 |
van prooijen | 3 |
although twitter | 3 |
raw count | 3 |
disease community | 3 |
frame included | 3 |
also rallied | 3 |
regular user | 3 |
systematic mapping | 3 |
closures group | 3 |
similarity measures | 3 |
recognition software | 3 |
endoscopic interventions | 3 |
feature space | 3 |
top bigrams | 3 |
moral contests | 3 |
received relatively | 3 |
response time | 3 |
score greater | 3 |
users according | 3 |
conventional media | 3 |
feb th | 3 |
across time | 3 |
methodology used | 3 |
network featured | 3 |
tweet based | 3 |
user behaviours | 3 |
labeling approach | 3 |
binary accuracy | 3 |
section iii | 3 |
two variables | 3 |
data dictionary | 3 |
size conferred | 3 |
become essential | 3 |
enabled us | 3 |
political analysis | 3 |
gastroenterology fellows | 3 |
hospital beds | 3 |
media disinformation | 3 |
north korea | 3 |
one side | 3 |
learning classifier | 3 |
introductory paragraph | 3 |
web services | 3 |
fraudulent financial | 3 |
medical students | 3 |
table illustrates | 3 |
overall pattern | 3 |
complementary data | 3 |
law model | 3 |
recent analysis | 3 |
computational model | 3 |
normal users | 3 |
higher accuracy | 3 |
spatial decision | 3 |
social platform | 3 |
local health | 3 |
topics across | 3 |
predictor set | 3 |
hybrid approach | 3 |
outbreak classes | 3 |
related keywords | 3 |
government decision | 3 |
found containing | 3 |
conservatism scale | 3 |
best topic | 3 |
identify shifting | 3 |
right quadrant | 3 |
usage graph | 3 |
geographical region | 3 |
fan motivations | 3 |
research will | 3 |
contour lines | 3 |
il fatto | 3 |
may change | 3 |
current research | 3 |
epidemic research | 3 |
dataset contains | 3 |
knowledge base | 3 |
media analytics | 3 |
collective alarm | 3 |
study based | 3 |
encountered clinical | 3 |
structured data | 3 |
least two | 3 |
disk i | 3 |
social influence | 3 |
may require | 3 |
maximum number | 3 |
link increased | 3 |
two indicators | 3 |
available datasets | 3 |
hadoop distributed | 3 |
supervised learning | 3 |
provide insights | 3 |
positive samples | 3 |
good balance | 3 |
study will | 3 |
status id | 3 |
media provides | 3 |
specific subject | 3 |
static prediction | 3 |
information operation | 3 |
examined whether | 3 |
unique tweets | 3 |
varies across | 3 |
entity instance | 3 |
transition point | 3 |
news data | 3 |
location data | 3 |
cambridge analytica | 3 |
bot activity | 3 |
network graph | 3 |
noun phrases | 3 |
prominent politicians | 3 |
features extracted | 3 |
emerging topics | 3 |
like facebook | 3 |
also received | 3 |
either positive | 3 |
continuing medical | 3 |
mps using | 3 |
main stages | 3 |
susceptible agents | 3 |
time intervals | 3 |
cent army | 3 |
containing urls | 3 |
massive relevance | 3 |
general population | 3 |
billion tweets | 3 |
time credit | 3 |
intensively use | 3 |
relevant words | 3 |