prompt_front: You are given a scientific article and a question. Answer the question as concisely as you can, using a single phrase or sentence if possible. If the question cannot be answered based on the information in the article, write "unanswerable". If the question is a yes/no question, answer "yes", "no", or "unanswerable". Do not provide any explanation.

Please answer the questions based on the text provided below. How do they obtain psychological dimensions of people?
Context 1: Introduction
Blogging gained momentum in 1999 and became especially popular after the launch of freely available, hosted platforms such as blogger com or livejournal com  Blogging has progressively been used by individuals to share news, ideas, and information, but it has also developed a mainstream role to the extent that it is being used by political consultants and news services as a tool for outreach and opinion forming as well as by businesses as a marketing tool to promote products and services BIBREF0  
For this paper, we compiled a very large geolocated collection of blogs, written by individuals located in the U S , with the purpose of creating insightful mappings of the blogging community  In particular, during May-July 2015, we gathered the profile information for all the users that have self-reported their location in the U S , along with a number of posts for all their associated blogs  We utilize this blog collection to generate maps of the U S  that reflect user demographics, language use, and distributions of psycholinguistic and semantic word classes  We believe that these maps can provide valuable insights and partial verification of previous claims in support of research in linguistic geography BIBREF1 , regional personality BIBREF2 , and language analysis BIBREF3 , BIBREF4 , as well as psychology and its relation to human geography BIBREF5  
Data Collection
Our premise is that we can generate informative maps using geolocated information available on social media; therefore, we guide the blog collection process with the constraint that we only accept blogs that have specific location information  Moreover, we aim to find blogs belonging to writers from all 50 U S  states, which will allow us to build U S  maps for various dimensions of interest 
We first started by collecting a set of profiles of bloggers that met our location specifications by searching individual states on the profile finder on http://www blogger com  Starting with this list, we can locate the profile page for a user, and subsequently extract additional information, which includes fields such as name, email, occupation, industry, and so forth  It is important to note that the profile finder only identifies users that have an exact match to the location specified in the query; we thus built and ran queries that used both state abbreviations (e g , TX, AL), as well as the states' full names (e g , Texas, Alabama) 
After completing all the processing steps, we identified 197,527 bloggers with state location information  For each of these bloggers, we found their blogs (note that a blogger can have multiple blogs), for a total of 335,698 blogs  For each of these blogs, we downloaded the 21 most recent blog postings, which were cleaned of HTML tags and tokenized, resulting in a collection of 4,600,465 blog posts 
Maps from Blogs
Our dataset provides mappings between location, profile information, and language use, which we can leverage to generate maps that reflect demographic, linguistic, and psycholinguistic properties of the population represented in the dataset 
People Maps
The first map we generate depicts the distribution of the bloggers in our dataset across the U S  Figure FIGREF1 shows the density of users in our dataset in each of the 50 states  For instance, the densest state was found to be California with 11,701 users  The second densest is Texas, with 9,252 users, followed by New York, with 9,136  The state with the fewest bloggers is Delaware with 1,217 users  Not surprisingly, this distribution correlates well with the population of these states, with a Spearman's rank correlation INLINEFORM0 of 0 91 and a p-value INLINEFORM1 0 0001, and is very similar to the one reported in Lin and Halavais Lin04 
Figure FIGREF1 also shows the cities mentioned most often in our dataset  In particular, it illustrates all 227 cities that have at least 100 bloggers  The bigger the dot on the map, the larger the number of users found in that city  The five top blogger-dense cities, in order, are: Chicago, New York, Portland, Seattle, and Atlanta 
We also generate two maps that delineate the gender distribution in the dataset  Overall, the blogging world seems to be dominated by females: out of 153,209 users who self-reported their gender, only 52,725 are men and 100,484 are women  Figures FIGREF1 and FIGREF1 show the percentage of male and female bloggers in each of the 50 states  As seen in this figure, there are more than the average number of male bloggers in states such as California and New York, whereas Utah and Idaho have a higher percentage of women bloggers 
Another profile element that can lead to interesting maps is the Industry field BIBREF6   Using this field, we created different maps that plot the geographical distribution of industries across the country  As an example, Figure FIGREF2 shows the percentage of the users in each state working in the automotive and tourism industries respectively 
Linguistic Maps
Another use of the information found in our dataset is to build linguistic maps, which reflect the geographic lexical variation across the 50 states BIBREF7   We generate maps that represent the relative frequency by which a word occurs in the different states  Figure FIGREF3 shows sample maps created for two different words  The figure shows the map generated for one location specific word, Maui, which unsurprisingly is found predominantly in Hawaii, and a map for a more common word, lake, which has a high occurrence rate in Minnesota (Land of 10,000 Lakes) and Utah (home of the Great Salt Lake)  Our demo described in Section SECREF4 , can also be used to generate maps for function words, which can be very telling regarding people's personality BIBREF8  
Psycholinguistic and Semantic Maps
LIWC  In addition to individual words, we can also create maps for word categories that reflect a certain psycholinguistic or semantic property  Several lexical resources, such as Roget or Linguistic Inquiry and Word Count BIBREF9 , group words into categories  Examples of such categories are Money, which includes words such as remuneration, dollar, and payment; or Positive feelings with words such as happy, cheerful, and celebration  Using the distribution of the individual words in a category, we can compile distributions for the entire category, and therefore generate maps for these word categories  For instance, figure FIGREF8 shows the maps created for two categories: Positive Feelings and Money  The maps are not surprising, and interestingly they also reflect an inverse correlation between Money and Positive Feelings  
Values  We also measure the usage of words related to people's core values as reported by Boyd et al  boyd2015  The sets of words, or themes, were excavated using the Meaning Extraction Method (MEM) BIBREF10   MEM is a topic modeling approach applied to a corpus of texts created by hundreds of survey respondents from the U S  who were asked to freely write about their personal values  To illustrate, Figure FIGREF9 shows the geographical distributions of two of these value themes: Religion and Hard Work  Southeastern states often considered as the nation's “Bible Belt” BIBREF11 were found to have generally higher usage of Religion words such as God, bible, and church  Another broad trend was that western-central states (e g , Wyoming, Nebraska, Iowa) commonly blogged about Hard Work, using words such as hard, work, and job more often than bloggers in other regions 
Web Demonstration
A prototype, interactive charting demo is available at http://lit eecs umich edu/~geoliwc/  In addition to drawing maps of the geographical distributions on the different LIWC categories, the tool can report the three most and least correlated LIWC categories in the U S  and compare the distributions of any two categories 
Conclusions
In this paper, we showed how we can effectively leverage a prodigious blog dataset  Not only does the dataset bring out the extensive linguistic content reflected in the blog posts, but also includes location information and rich metadata  These data allow for the generation of maps that reflect the demographics of the population, variations in language use, and differences in psycholinguistic and semantic categories  These mappings can be valuable to both psychologists and linguists, as well as lexicographers  A prototype demo has been made available together with the code used to collect our dataset 
Acknowledgments
This material is based in part upon work supported by the National Science Foundation (#1344257) and by the John Templeton Foundation (#48503)  Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation or the John Templeton Foundation  We would like to thank our colleagues Hengjing Wang, Jiatao Fan, Xinghai Zhang, and Po-Jung Huang who provided technical help with the implementation of the demo 
Context 2: Introduction
Nowadays deep learning techniques outperform the other conventional methods in most of the speech-related tasks  Training robust deep neural networks for each task depends on the availability of powerful processing GPUs, as well as standard and large scale datasets  In text-independent speaker verification, large-scale datasets are available, thanks to the NIST SRE evaluations and other data collection projects such as VoxCeleb BIBREF0 
In text-dependent speaker recognition, experiments with end-to-end architectures conducted on large proprietary databases have demonstrated their superiority over traditional approaches BIBREF1  Yet, contrary to text-independent speaker recognition, text-dependent speaker recognition lacks large-scale publicly available databases  The two most well-known datasets are probably RSR2015 BIBREF2 and RedDots BIBREF3  The former contains speech data collected from 300 individuals in a controlled manner, while the latter is used primarily for evaluation rather than training, due to its small number of speakers (only 64)  Motivated by this lack of large-scale dataset for text-dependent speaker verification, we chose to proceed with the collection of the DeepMine dataset, which we expect to become a standard benchmark for the task 
Apart from speaker recognition, large amounts of training data are required also for training automatic speech recognition (ASR) systems  Such datasets should not only be large in size, they should also be characterized by high variability with respect to speakers, age and dialects  While several datasets with these properties are available for languages like English, Mandarin, French, this is not the case for several other languages, such as Persian  To this end, we proceeded with collecting a large-scale dataset, suitable for building robust ASR models in Persian 
The main goal of the DeepMine project was to collect speech from at least a few thousand speakers, enabling research and development of deep learning methods  The project started at the beginning of 2017, and after designing the database and the developing Android and server applications, the data collection began in the middle of 2017  The project finished at the end of 2018 and the cleaned-up and final version of the database was released at the beginning of 2019  In BIBREF4, the running project and its data collection scenarios were described, alongside with some preliminary results and statistics  In this paper, we announce the final and cleaned-up version of the database, describe its different parts and provide various evaluation setups for each part  Finally, since the database was designed mainly for text-dependent speaker verification purposes, some baseline results are reported for this task on the official evaluation setups  Additional baseline results are also reported for Persian speech recognition  However, due to the space limitation in this paper, the baseline results are not reported for all the database parts and conditions  They will be defined and reported in the database technical documentation and in a future journal paper 
Data Collection
DeepMine is publicly available for everybody with a variety of licenses for different users  It was collected using crowdsourcing BIBREF4  The data collection was done using an Android application  Each respondent installed the application on his/her personal device and recorded several phrases in different sessions  The Android application did various checks on each utterance and if it passed all of them, the respondent was directed to the next phrase  For more information about data collection scenario, please refer to BIBREF4 
Data Collection ::: Post-Processing
In order to clean-up the database, the main post-processing step was to filter out problematic utterances  Possible problems include speaker word insertions (e g  repeating some part of a phrase), deletions, substitutions, and involuntary disfluencies  To detect these, we implemented an alignment stage, similar to the second alignment stage in the LibriSpeech project BIBREF5  In this method, a custom decoding graph was generated for each phrase  The decoding graph allows for word skipping and word insertion in the phrase 
For text-dependent and text-prompted parts of the database, such errors are not allowed  Hence, any utterances with errors were removed from the enrollment and test lists  For the speech recognition part, a sub-part of the utterance which is correctly aligned to the corresponding transcription is kept  After the cleaning step, around 190 thousand utterances with full transcription and 10 thousand with sub-part alignment have remained in the database 
Data Collection ::: Statistics
After processing the database and removing problematic respondents and utterances, 1969 respondents remained in the database, with 1149 of them being male and 820 female  297 of the respondents could not read English and have therefore read only the Persian prompts  About 13200 sessions were recorded by females and similarly, about 9500 sessions by males, i e  women are over-represented in terms of sessions, even though their number is 17% smaller than that of males  Other useful statistics related to the database are shown in Table TABREF4 
The last status of the database, as well as other related and useful information about its availability can be found on its website, together with a limited number of samples 
DeepMine Database Parts
The DeepMine database consists of three parts  The first one contains fixed common phrases to perform text-dependent speaker verification  The second part consists of random sequences of words useful for text-prompted speaker verification, and the last part includes phrases with word- and phoneme-level transcription, useful for text-independent speaker verification using a random phrase (similar to Part4 of RedDots)  This part can also serve for Persian ASR training  Each part is described in more details below  Table TABREF11 shows the number of unique phrases in each part of the database  For the English text-dependent part, the following phrases were selected from part1 of the RedDots database, hence the RedDots can be used as an additional training set for this part:
“My voice is my password ”
“OK Google ”
“Artificial intelligence is for real ”
“Actions speak louder than words ”
“There is no such thing as a free lunch ”
DeepMine Database Parts ::: Part1 - Text-dependent (TD)
This part contains a set of fixed phrases which are used to verify speakers in text-dependent mode  Each speaker utters 5 Persian phrases, and if the speaker can read English, 5 phrases selected from Part1 of the RedDots database are also recorded 
We have created three experimental setups with different numbers of speakers in the evaluation set  For each setup, speakers with more recording sessions are included in the evaluation set and the rest of the speakers are used for training in the background set (in the database, all background sets are basically training data)  The rows in Table TABREF13 corresponds to the different experimental setups and shows the numbers of speakers in each set  Note that, for English, we have filtered the (Persian native) speakers by the ability to read English  Therefore, there are fewer speakers in each set for English than for Persian  There is a small “dev” set in each setup which can be used for parameter tuning to prevent over-tuning on the evaluation set 
For each experimental setup, we have defined several official trial lists with different numbers of enrollment utterances per trial in order to investigate the effects of having different amounts of enrollment data  All trials in one trial list have the same number of enrollment utterances (3 to 6) and only one test utterance  All enrollment utterances in a trial are taken from different consecutive sessions and the test utterance is taken from yet another session  From all the setups and conditions, the 100-spk with 3-session enrollment (3-sess) is considered as the main evaluation condition  In Table TABREF14, the number of trials for Persian 3-sess are shown for the different types of trial in the text-dependent speaker verification (SV)  Note that for Imposter-Wrong (IW) trials (i e  imposter speaker pronouncing wrong phrase), we merely create one wrong trial for each Imposter-Correct (IC) trial to limit the huge number of possible trials for this case  So, the number of trials for IC and IW cases are the same 
DeepMine Database Parts ::: Part2 - Text-prompted (TP)
For this part, in each session, 3 random sequences of Persian month names are shown to the respondent in two modes: In the first mode, the sequence consists of all 12 months, which will be used for speaker enrollment  The second mode contains a sequence of 3 month names that will be used as a test utterance  In each 8 sessions received by a respondent from the server, there are 3 enrollment phrases of all 12 months (all in just one session), and $7 \times 3$ other test phrases, containing fewer words  For a respondent who can read English, 3 random sequences of English digits are also recorded in each session  In one of the sessions, these sequences contain all digits and the remaining ones contain only 4 digits 
Similar to the text-dependent case, three experimental setups with different number of speaker in the evaluation set are defined (corresponding to the rows in Table TABREF16)  However, different strategy is used for defining trials: Depending on the enrollment condition (1- to 3-sess), trials are enrolled on utterances of all words from 1 to 3 different sessions (i e  3 to 9 utterances)  Further, we consider two conditions for test utterances: seq test utterance with only 3 or 4 words and full test utterances with all words (i e  same words as in enrollment but in different order)  From all setups an all conditions, the 100-spk with 1-session enrolment (1-sess) is considered as the main evaluation condition for the text-prompted case  In Table TABREF16, the numbers of trials (sum for both seq and full conditions) for Persian 1-sess are shown for the different types of trials in the text-prompted SV  Again, we just create one IW trial for each IC trial 
DeepMine Database Parts ::: Part3 - Text-independent (TI)
In this part, 8 Persian phrases that have already been transcribed on the phone level are displayed to the respondent  These phrases are chosen mostly from news and Persian Wikipedia  If the respondent is unable to read English, instead of 5 fixed phrases and 3 random digit strings, 8 other Persian phrases are also prompted to the respondent to have exactly 24 phrases in each recording session 
This part can be useful at least for three potential applications  First, it can be used for text-independent speaker verification  The second application of this part (same as Part4 of RedDots) is text-prompted speaker verification using random text (instead of a random sequence of words)  Finally, the third application is large vocabulary speech recognition in Persian (explained in the next sub-section) 
Based on the recording sessions, we created two experimental setups for speaker verification  In the first one, respondents with at least 17 recording sessions are included to the evaluation set, respondents with 16 sessions to the development and the rest of respondents to the background set (can be used as training data)  In the second setup, respondents with at least 8 sessions are included to the evaluation set, respondents with 6 or 7 sessions to the development and the rest of respondents to the background set  Table TABREF18 shows numbers of speakers in each set of the database for text-independent SV case 
For text-independent SV, we have considered 4 scenarios for enrollment and 4 scenarios for test  The speaker can be enrolled using utterances from 1, 2 or 3 consecutive sessions (1sess to 3sess) or using 8 utterances from 8 different sessions  The test speech can be one utterance (1utt) for short duration scenario or all utterances in one session (1sess) for long duration case  In addition, test speech can be selected from 5 English phrases for cross-language testing (enrollment using Persian utterances and test using English utterances)
Context 3: 10pt
1 10pt
[ Characterizing Political Fake News in Twitter by its Meta-DataJulio Amador Díaz LópezAxel Oehmichen Miguel Molina-Solana( j amador, axelfrancois oehmichen11, mmolinas@imperial ac uk ) Imperial College London This article presents a preliminary approach towards characterizing political fake news on Twitter through the analysis of their meta-data  In particular, we focus on more than 1 5M tweets collected on the day of the election of Donald Trump as 45th president of the United States of America  We use the meta-data embedded within those tweets in order to look for differences between tweets containing fake news and tweets not containing them  Specifically, we perform our analysis only on tweets that went viral, by studying proxies for users' exposure to the tweets, by characterizing accounts spreading fake news, and by looking at their polarization  We found significant differences on the distribution of followers, the number of URLs on tweets, and the verification of the users 
]
Introduction
While fake news, understood as deliberately misleading pieces of information, have existed since long ago (e g  it is not unusual to receive news falsely claiming the death of a celebrity), the term reached the mainstream, particularly so in politics, during the 2016 presidential election in the United States BIBREF0   Since then, governments and corporations alike (e g  Google BIBREF1 and Facebook BIBREF2 ) have begun efforts to tackle fake news as they can affect political decisions BIBREF3   Yet, the ability to define, identify and stop fake news from spreading is limited 
Since the Obama campaign in 2008, social media has been pervasive in the political arena in the United States  Studies report that up to 62% of American adults receive their news from social media BIBREF4   The wide use of platforms such as Twitter and Facebook has facilitated the diffusion of fake news by simplifying the process of receiving content with no significant third party filtering, fact-checking or editorial judgement  Such characteristics make these platforms suitable means for sharing news that, disguised as legit ones, try to confuse readers 
Such use and their prominent rise has been confirmed by Craig Silverman, a Canadian journalist who is a prominent figure on fake news BIBREF5 : “In the final three months of the US presidential campaign, the top-performing fake election news stories on Facebook generated more engagement than the top stories from major news outlet” 
Our current research hence departs from the assumption that social media is a conduit for fake news and asks the question of whether fake news (as spam was some years ago) can be identified, modelled and eventually blocked  In order to do so, we use a sample of more that 1 5M tweets collected on November 8th 2016 —election day in the United States— with the goal of identifying features that tweets containing fake news are likely to have  As such, our paper aims to provide a preliminary characterization of fake news in Twitter by looking into meta-data embedded in tweets  Considering meta-data as a relevant factor of analysis is in line with findings reported by Morris et al  BIBREF6   We argue that understanding differences between tweets containing fake news and regular tweets will allow researchers to design mechanisms to block fake news in Twitter 
Specifically, our goals are: 1) compare the characteristics of tweets labelled as containing fake news to tweets labelled as not containing them, 2) characterize, through their meta-data, viral tweets containing fake news and the accounts from which they originated, and 3) determine the extent to which tweets containing fake news expressed polarized political views 
For our study, we used the number of retweets to single-out those that went viral within our sample  Tweets within that subset (viral tweets hereafter) are varied and relate to different topics  We consider that a tweet contains fake news if its text falls within any of the following categories described by Rubin et al  BIBREF7 (see next section for the details of such categories): serious fabrication, large-scale hoaxes, jokes taken at face value, slanted reporting of real facts and stories where the truth is contentious  The dataset BIBREF8 , manually labelled by an expert, has been publicly released and is available to researchers and interested parties 
From our results, the following main observations can be made:
Our findings resonate with similar work done on fake news such as the one from Allcot and Gentzkow BIBREF9   Therefore, even if our study is a preliminary attempt at characterizing fake news on Twitter using only their meta-data, our results provide external validity to previous research  Moreover, our work not only stresses the importance of using meta-data, but also underscores which parameters may be useful to identify fake news on Twitter 
The rest of the paper is organized as follows  The next section briefly discusses where this work is located within the literature on fake news and contextualizes the type of fake news we are studying  Then, we present our hypotheses, the data, and the methodology we follow  Finally, we present our findings, conclusions of this study, and future lines of work 
Defining Fake news
Our research is connected to different strands of academic knowledge related to the phenomenon of fake news  In relation to Computer Science, a recent survey by Conroy and colleagues BIBREF10 identifies two popular approaches to single-out fake news  On the one hand, the authors pointed to linguistic approaches consisting in using text, its linguistic characteristics and machine learning techniques to automatically flag fake news  On the other, these researchers underscored the use of network approaches, which make use of network characteristics and meta-data, to identify fake news 
With respect to social sciences, efforts from psychology, political science and sociology, have been dedicated to understand why people consume and/or believe misinformation BIBREF11 , BIBREF12 , BIBREF13 , BIBREF14   Most of these studies consistently reported that psychological biases such as priming effects and confirmation bias play an important role in people ability to discern misinformation 
In relation to the production and distribution of fake news, a recent paper in the field of Economics BIBREF9 found that most fake news sites use names that resemble those of legitimate organizations, and that sites supplying fake news tend to be short-lived  These authors also noticed that fake news items are more likely shared than legitimate articles coming from trusted sources, and they tend to exhibit a larger level of polarization 
The conceptual issue of how to define fake news is a serious and unresolved issue  As the focus of our work is not attempting to offer light on this, we will rely on work by other authors to describe what we consider as fake news  In particular, we use the categorization provided by Rubin et al  BIBREF7   The five categories they described, together with illustrative examples from our dataset, are as follows:
Research Hypotheses
Previous works on the area (presented in the section above) suggest that there may be important determinants for the adoption and diffusion of fake news  Our hypotheses builds on them and identifies three important dimensions that may help distinguishing fake news from legit information:
Taking those three dimensions into account, we propose the following hypotheses about the features that we believe can help to identify tweets containing fake news from those not containing them  They will be later tested over our collected dataset 
Exposure 
Characterization 
Polarization 
Data and Methodology
For this study, we collected publicly available tweets using Twitter's public API  Given the nature of the data, it is important to emphasize that such tweets are subject to Twitter's terms and conditions which indicate that users consent to the collection, transfer, manipulation, storage, and disclosure of data  Therefore, we do not expect ethical, legal, or social implications from the usage of the tweets  Our data was collected using search terms related to the presidential election held in the United States on November 8th 2016  Particularly, we queried Twitter's streaming API, more precisely the filter endpoint of the streaming API, using the following hashtags and user handles: #MyVote2016, #ElectionDay, #electionnight, @realDonaldTrump and @HillaryClinton  The data collection ran for just one day (Nov 8th 2016) 
One straightforward way of sharing information on Twitter is by using the retweet functionality, which enables a user to share a exact copy of a tweet with his followers  Among the reasons for retweeting, Body et al  BIBREF15 reported the will to: 1) spread tweets to a new audience, 2) to show one’s role as a listener, and 3) to agree with someone or validate the thoughts of others  As indicated, our initial interest is to characterize tweets containing fake news that went viral (as they are the most harmful ones, as they reach a wider audience), and understand how it differs from other viral tweets (that do not contain fake news)  For our study, we consider that a tweet went viral if it was retweeted more than 1000 times 
Once we have the dataset of viral tweets, we eliminated duplicates (some of the tweets were collected several times because they had several handles) and an expert manually inspected the text field within the tweets to label them as containing fake news, or not containing them (according to the characterization presented before)  This annotated dataset BIBREF8 is publicly available and can be freely reused 
Finally, we use the following fields within tweets (from the ones returned by Twitter's API) to compare their distributions and look for differences between viral tweets containing fake news and viral tweets not containing fake news:
In the following section, we provide graphical descriptions of the distribution of each of the identified attributes for the two sets of tweets (those labelled as containing fake news and those labelled as not containing them)  Where appropriate, we normalized and/or took logarithms of the data for better representation  To gain a better understanding of the significance of those differences, we use the Kolmogorov-Smirnov test with the null hypothesis that both distributions are equal 
Results
The sample collected consisted on 1 785 855 tweets published by 848 196 different users  Within our sample, we identified 1327 tweets that went viral (retweeted more than 1000 times by the 8th of November 2016) produced by 643 users  Such small subset of viral tweets were retweeted on 290 841 occasions in the observed time-window 
The 1327 `viral' tweets were manually annotated as containing fake news or not  The annotation was carried out by a single person in order to obtain a consistent annotation throughout the dataset  Out of those 1327 tweets, we identified 136 as potentially containing fake news (according to the categories previously described), and the rest were classified as `non containing fake news'  Note that the categorization is far from being perfect given the ambiguity of fake news themselves and human judgement involved in the process of categorization  Because of this, we do not claim that this dataset can be considered a ground truth 
The following results detail characteristics of these tweets along the previously mentioned dimensions  Table TABREF23 reports the actual differences (together with their associated p-values) of the distributions of viral tweets containing fake news and viral tweets not containing them for every variable considered 
Exposure
Figure FIGREF24 shows that, in contrast to other kinds of viral tweets, those containing fake news were created more recently  As such, Twitter users were exposed to fake news related to the election for a shorter period of time 
However, in terms of retweets, Figure FIGREF25 shows no apparent difference between containing fake news or not containing them  That is confirmed by the Kolmogorov-Smirnoff test, which does not discard the hypothesis that the associated distributions are equal 
In relation to the number of favourites, users that generated at least a viral tweet containing fake news appear to have, on average, less favourites than users that do not generate them  Figure FIGREF26 shows the distribution of favourites  Despite the apparent visual differences, the difference are not statistically significant 
Finally, the number of hashtags used in viral fake news appears to be larger than those in other viral tweets  Figure FIGREF27 shows the density distribution of the number of hashtags used  However, once again, we were not able to find any statistical difference between the average number of hashtags in a viral tweet and the average number of hashtags in viral fake news 
Characterization
We found that 82 users within our sample were spreading fake news (i e  they produced at least one tweet which was labelled as fake news)  Out of those, 34 had verified accounts, and the rest were unverified  From the 48 unverified accounts, 6 have been suspended by Twitter at the date of writing, 3 tried to imitate legitimate accounts of others, and 4 accounts have been already deleted  Figure FIGREF28 shows the proportion of verified accounts to unverified accounts for viral tweets (containing fake news vs  not containing fake news)  From the chart, it is clear that there is a higher chance of fake news coming from unverified accounts 
Turning to friends, accounts distributing fake news appear to have, on average, the same number of friends than those distributing tweets with no fake news  However, the density distribution of friends from the accounts (Figure FIGREF29 ) shows that there is indeed a statistically significant difference in their distributions 
If we take into consideration the number of followers, accounts generating viral tweets with fake news do have a very different distribution on this dimension, compared to those accounts generating viral tweets with no fake news (see Figure FIGREF30 )  In fact, such differences are statistically significant 
A useful representation for friends and followers is the ratio between friends/followers  Figures FIGREF31 and FIGREF32 show this representation  Notice that accounts spreading viral tweets with fake news have, on average, a larger ratio of friends/followers  The distribution of those accounts not generating fake news is more evenly distributed 
With respect to the number of mentions, Figure FIGREF33 shows that viral tweets labelled as containing fake news appear to use mentions to other users less frequently than viral tweets not containing fake news  In other words, tweets containing fake news mostly contain 1 mention, whereas other tweets tend to have two)  Such differences are statistically significant 
The analysis (Figure FIGREF34 ) of the presence of media in the tweets in our dataset shows that tweets labelled as not containing fake news appear to present more media elements than those labelled as fake news  However, the difference is not statistically significant 
On the other hand, Figure FIGREF35 shows that viral tweets containing fake news appear to include more URLs to other sites than viral tweets that do not contain fake news  In fact, the difference between the two distributions is statistically significant (assuming INLINEFORM0 ) 
Polarization
Finally, manual inspection of the text field of those viral tweets labelled as containing fake news shows that 117 of such tweets expressed support for Donald Trump, while only 8 supported Hillary Clinton  The remaining tweets contained fake news related to other topics, not expressing support for any of the candidates 
Discussion
As a summary, and constrained by our existing dataset, we made the following observations regarding differences between viral tweets labelled as containing fake news and viral tweets labelled as not containing them:
These findings (related to our initial hypothesis in Table TABREF44 ) clearly suggest that there are specific pieces of meta-data about tweets that may allow the identification of fake news  One such parameter is the time of exposure  Viral tweets containing fake news are shorter-lived than those containing other type of content  This notion seems to resonate with our findings showing that a number of accounts spreading fake news have already been deleted or suspended by Twitter by the time of writing  If one considers that researchers using different data have found similar results BIBREF9 , it appears that the lifetime of accounts, together with the age of the questioned viral content could be useful to identify fake news  In the light of this finding, accounts newly created should probably put under higher scrutiny than older ones  This in fact, would be a nice a-priori bias for a Bayesian classifier 
Accounts spreading fake news appear to have a larger proportion of friends/followers (i e  they have, on average, the same number of friends but a smaller number of followers) than those spreading viral content only  Together with the fact that, on average, tweets containing fake news have more URLs than those spreading viral content, it is possible to hypothesize that, both, the ratio of friends/followers of the account producing a viral tweet and number of URLs contained in such a tweet could be useful to single-out fake news in Twitter  Not only that, but our finding related to the number of URLs is in line with intuitions behind the incentives to create fake news commonly found in the literature BIBREF9 (in particular that of obtaining revenue through click-through advertising) 
Finally, it is interesting to notice that the content of viral fake news was highly polarized  This finding is also in line with those of Alcott et al  BIBREF9   This feature suggests that textual sentiment analysis of the content of tweets (as most researchers do), together with the above mentioned parameters from meta-data, may prove useful for identifying fake news 
Conclusions
With the election of Donald Trump as President of the United States, the concept of fake news has become a broadly-known phenomenon that is getting tremendous attention from governments and media companies  We have presented a preliminary study on the meta-data of a publicly available dataset of tweets that became viral during the day of the 2016 US presidential election  Our aim is to advance the understanding of which features might be characteristic of viral tweets containing fake news in comparison with viral tweets without fake news 
We believe that the only way to automatically identify those deceitful tweets (i e  containing fake news) is by actually understanding and modelling them  Only then, the automation of the processes of tagging and blocking these tweets can be successfully performed  In the same way that spam was fought, we anticipate fake news will suffer a similar evolution, with social platforms implementing tools to deal with them  With most works so far focusing on the actual content of the tweets, ours is a novel attempt from a different, but also complementary, angle 
Within the used dataset, we found there are differences around exposure, characteristics of accounts spreading fake news and the tone of the content  Those findings suggest that it is indeed possible to model and automatically detect fake news  We plan to replicate and validate our experiments in an extended sample of tweets (until 4 months after the US election), and tests the predictive power of the features we found relevant within our sample 
Author Disclosure Statement
No competing financial interest exist 

ground_truth: using the Meaning Extraction Method

Unanswerable

prompt_front: You are given a scientific article and a question. Answer the question as concisely as you can, using a single phrase or sentence if possible. If the question cannot be answered based on the information in the article, write "unanswerable". If the question is a yes/no question, answer "yes", "no", or "unanswerable". Do not provide any explanation.

Please answer the questions based on the text provided below. what accents are present in the corpus?
Context 2:  From all setups, 1sess-1utt and 1sess-1sess for 438-spk set are considered as the main evaluation setups for text-independent case  Table TABREF19 shows number of trials for these setups 
For text-prompted SV with random text, the same setup as text-independent case together with corresponding utterance transcriptions can be used 
DeepMine Database Parts ::: Part3 - Speech Recognition
As explained before, Part3 of the DeepMine database can be used for Persian read speech recognition  There are only a few databases for speech recognition in Persian BIBREF6, BIBREF7  Hence, this part can at least partly address this problem and enable robust speech recognition applications in Persian  Additionally, it can be used for speaker recognition applications, such as training deep neural networks (DNNs) for extracting bottleneck features BIBREF8, or for collecting sufficient statistics using DNNs for i-vector training 
We have randomly selected 50 speakers (25 for each gender) from the all speakers in the database which have net speech (without silence parts) between 25 minutes to 50 minutes as test speakers  For each speaker, the utterances in the first 5 sessions are included to (small) test-set and the other utterances of test speakers are considered as a large-test-set  The remaining utterances of the other speakers are included in the training set  The test-set, large-test-set and train-set contain 5 9, 28 5 and 450 hours of speech respectively 
There are about 8300 utterances in Part3 which contain only Persian full names (i e  first and family name pairs)  Each phrase consists of several full names and their phoneme transcriptions were extracted automatically using a trained Grapheme-to-Phoneme (G2P)  These utterances can be used to evaluate the performance of a systems for name recognition, which is usually more difficult than the normal speech recognition because of the lack of a reliable language model 
Experiments and Results
Due to the space limitation, we present results only for the Persian text-dependent speaker verification and speech recognition 
Experiments and Results ::: Speaker Verification Experiments
We conducted an experiment on text-dependent speaker verification part of the database, using the i-vector based method proposed in BIBREF9, BIBREF10 and applied it to the Persian portion of Part1  In this experiment, 20-dimensional MFCC features along with first and second derivatives are extracted from 16 kHz signals using HTK BIBREF11 with 25 ms Hamming windowed frames with 15 ms overlap 
The reported results are obtained with a 400-dimensional gender independent i-vector based system  The i-vectors are first length-normalized and are further normalized using phrase- and gender-dependent Regularized Within-Class Covariance Normalization (RWCCN) BIBREF10  Cosine distance is used to obtain speaker verification scores and phrase- and gender-dependent s-norm is used for normalizing the scores  For aligning speech frames to Gaussian components, monophone HMMs with 3 states and 8 Gaussian components in each state are used BIBREF10  We only model the phonemes which appear in the 5 Persian text-dependent phrases 
For speaker verification experiments, the results were reported in terms of Equal Error Rate (EER) and Normalized Detection Cost Function as defined for NIST SRE08 ($\mathrm {NDCF_{0 01}^{min}}$) and NIST SRE10 ($\mathrm {NDCF_{0 001}^{min}}$)  As shown in Table TABREF22, in text-dependent SV there are 4 types of trials: Target-Correct and Imposter-Correct refer to trials when the pass-phrase is uttered correctly by target and imposter speakers respectively, and in same manner, Target-Wrong and Imposter-Wrong refer to trials when speakers uttered a wrong pass-phrase  In this paper, only the correct trials (i e  Target-Correct as target trials vs Imposter-Correct as non-target trials) are considered for evaluating systems as it has been proved that these are the most challenging trials in text-dependent SV BIBREF8, BIBREF12 
Table TABREF23 shows the results of text-dependent experiments using Persian 100-spk and 3-sess setup  For filtering trials, the respondents' mobile brand and model were used in this experiment  In the table, the first two letters in the filter notation relate to the target trials and the second two letters (i e  right side of the colon) relate for non-target trials  For target trials, the first Y means the enrolment and test utterances were recorded using a device with the same brand by the target speaker  The second Y letter means both recordings were done using exactly the same device model  Similarly, the first Y for non-target trials means that the devices of target and imposter speakers are from the same brand (i e  manufacturer)  The second Y means that, in addition to the same brand, both devices have the same model  So, the most difficult target trials are “NN”, where the speaker has used different a device at the test time  In the same manner, the most difficult non-target trials which should be rejected by the system are “YY” where the imposter speaker has used the same device model as the target speaker (note that it does not mean physically the same device because each speaker participated in the project using a personal mobile device)  Hence, the similarity in the recording channel makes rejection more difficult 
The first row in Table TABREF23 shows the results for all trials  By comparing the results with the best published results on RSR2015 and RedDots BIBREF10, BIBREF8, BIBREF12, it is clear that the DeepMine database is more challenging than both RSR2015 and RedDots databases  For RSR2015, the same i-vector/HMM-based method with both RWCCN and s-norm has achieved EER less than 0 3% for both genders (Table VI in BIBREF10)  The conventional Relevance MAP adaptation with HMM alignment without applying any channel-compensation techniques (i e  without applying RWCCN and s-norm due to the lack of suitable training data) on RedDots Part1 for the male has achieved EER around 1 5% (Table XI in BIBREF10)  It is worth noting that EERs for DeepMine database without any channel-compensation techniques are 2 1 and 3 7% for males and females respectively 
One interesting advantage of the DeepMine database compared to both RSR2015 and RedDots is having several target speakers with more than one mobile device  This is allows us to analyse the effects of channel compensation methods  The second row in Table TABREF23 corresponds to the most difficult trials where the target trials come from mobile devices with different models while imposter trials come from the same device models  It is clear that severe degradation was caused by this kind of channel effects (i e  decreasing within-speaker similarities while increasing between-speaker similarities), especially for females 
The results in the third row show the condition when target speakers at the test time use exactly the same device that was used for enrollment  Comparing this row with the results in the first row proves how much improvement can be achieved when exactly the same device is used by the target speaker 
The results in the fourth row show the condition when imposter speakers also use the same device model at test time to fool the system  So, in this case, there is no device mismatch in all trials  By comparing the results with the third row, we can see how much degradation is caused if we only consider the non-target trials with the same device 
The fifth row shows similar results when the imposter speakers use device of the same brand as the target speaker but with a different model  Surprisingly, in this case, the degradation is negligible and it means that mobiles from a specific brand (manufacturer) have different recording channel properties 
The degraded female results in the sixth row as compared to the third row show the effect of using a different device model from the same brand for target trials  For males, the filters brings almost the same subsets of trials, which explains the very similar results in this case 
Looking at the first two and the last row of Table TABREF23, one can notice the significantly worse performance obtained for the female trials as compared to males  Note that these three rows include target trials where the devices used for enrollment do not necessarily match the devices used for recording test utterances  On the other hand, in rows 3 to 6, which exclude such mismatched trials, the performance for males and females is comparable  This suggest that the degraded results for females are caused by some problematic trials with device mismatch  The exact reason for this degradation is so far unclear and needs a further investigation 
In the last row of the table, the condition of the second row is relaxed: the target device should have different model possibly from the same brand and imposter device only needs to be from the same brand  In this case, as was expected, the performance degradation is smaller than in the second row 
Experiments and Results ::: Speech Recognition Experiments
In addition to speaker verification, we present several speech recognition experiments on Part3  The experiments were performed with the Kaldi toolkit BIBREF13  For training HMM-based MonoPhone model, only 20 thousands of shortest utterances are used and for other models the whole training data is used  The DNN based acoustic model is a time-delay DNN with low-rank factorized layers and skip connections without i-vector adaptation (a modified network from one of the best performing LibriSpeech recipes)  The network is shown in Table TABREF25: there are 16 F-TDNN layers, with dimension 1536 and linear bottleneck layers of dimension 256  The acoustic model is trained for 10 epochs using lattice-free maximum mutual information (LF-MMI) with cross-entropy regularization BIBREF14  Re-scoring is done using a pruned trigram language model and the size of the dictionary is around 90,000 words 
Table TABREF26 shows the results in terms of word error rate (WER) for different evaluated methods  As can be seen, the created database can be used to train well performing and practically usable Persian ASR models 
Conclusions
In this paper, we have described the final version of a large speech corpus, the DeepMine database  It has been collected using crowdsourcing and, according to the best of our knowledge, it is the largest public text-dependent and text-prompted speaker verification database in two languages: Persian and English  In addition, it is the largest text-independent speaker verification evaluation database, making it suitable to robustly evaluate state-of-the-art methods on different conditions  Alongside these appealing properties, it comes with phone-level transcription, making it suitable to train deep neural network models for Persian speech recognition 
We provided several evaluation protocols for each part of the database  The protocols allow researchers to investigate the performance of different methods in various scenarios and study the effects of channels, duration and phrase text on the performance  We also provide two test sets for speech recognition: One normal test set with a few minutes of speech for each speaker and one large test set with more (30 minutes on average) speech that can be used for any speaker adaptation method 
As baseline results, we reported the performance of an i-vector/HMM based method on Persian text-dependent part  Moreover, we conducted speech recognition experiments using conventional HMM-based methods, as well as state-of-the-art deep neural network based method using Kaldi toolkit with promising performance  Text-dependent results have shown that the DeepMine database is more challenging than RSR2015 and RedDots databases 
Acknowledgments
The data collection project was mainly supported by Sharif DeepMine company  The work on the paper was supported by Czech National Science Foundation (GACR) project "NEUREM3" No  19-26934X and the National Programme of Sustainability (NPU II) project "IT4Innovations excellence in science - LQ1602" 

ground_truth: Unanswerable

Unanswerable

prompt_front: You are given a scientific article and a question. Answer the question as concisely as you can, using a single phrase or sentence if possible. If the question cannot be answered based on the information in the article, write "unanswerable". If the question is a yes/no question, answer "yes", "no", or "unanswerable". Do not provide any explanation.

Please answer the questions based on the text provided below. What is their definition of tweets going viral?
Context 4:  Since our EEG-phonological pairwise classification has an internal structure involving individual phonemes and words, it seems to be a reasonable choice of classifier  The classifier receives its input from the latent vectors of the deep autoencoder and is trained in a supervised manner to output the final predicted classes corresponding to the speech imagery 
Dataset
We evaluate our model on a publicly available dataset, KARA ONE BIBREF17 , composed of multimodal data for stimulus-based, imagined and articulated speech state corresponding to 7 phonemic/syllabic ( /iy/, /piy/, /tiy/, /diy/, /uw/, /m/, /n/ ) as well as 4 words(pat, pot, knew and gnaw)  The dataset consists of 14 participants, with each prompt presented 11 times to each individual  Since our intention is to classify the phonological categories from human thoughts, we discard the facial and audio information and only consider the EEG data corresponding to imagined speech  It is noteworthy that given the mixed nature of EEG signals, it is reportedly challenging to attain a pairwise EEG-phoneme mapping BIBREF18   In order to explore the problem space, we thus specifically target five binary classification problems addressed in BIBREF17 , BIBREF18 , i e presence/absence of consonants, phonemic nasal, bilabial, high-front vowels and high-back vowels 
Training and hyperparameter selection
We performed two sets of experiments with the single-trial EEG data  In PHASE-ONE, our goals was to identify the best architectures and hyperparameters for our networks with a reasonable number of runs  For PHASE-ONE, we randomly shuffled and divided the data (1913 signals from 14 individuals) into train (80%), development (10%) and test sets (10%)  In PHASE-TWO, in order to perform a fair comparison with the previous methods reported on the same dataset, we perform a leave-one-subject out cross-validation experiment using the best settings we learn from PHASE-ONE 
The architectural parameters and hyperparameters listed in Table TABREF6 were selected through an exhaustive grid-search based on the validation set of PHASE-ONE  We conducted a series of empirical studies starting from single hidden-layered networks for each of the blocks and, based on the validation accuracy, we increased the depth of each given network and selected the optimal parametric set from all possible combinations of parameters  For the gradient boosting classification, we fixed the maximum depth at 10, number of estimators at 5000, learning rate at 0 1, regularization coefficient at 0 3, subsample ratio at 0 8, and column-sample/iteration at 0 4  We did not find any notable change of accuracy while varying other hyperparameters while training gradient boost classifier 
Performance analysis and discussion
To demonstrate the significance of the hierarchical CNN-LSTM-DAE method, we conducted separate experiments with the individual networks in PHASE-ONE of experiments and summarized the results in Table TABREF12 From the average accuracy scores, we observe that the mixed network performs much better than individual blocks which is in agreement with the findings in BIBREF21   A detailed analysis on repeated runs further shows that in most of the cases, LSTM alone does not perform better than chance  CNN, on the other hand, is heavily biased towards the class label which sees more training data corresponding to it  Though the situation improves with combined CNN-LSTM, our analysis clearly shows the necessity of a better encoding scheme to utilize the combined features rather than mere concatenation of the penultimate features of both networks 
The very fact that our combined network improves the classification accuracy by a mean margin of 14 45% than the CNN-LSTM network indeed reveals that the autoencoder contributes towards filtering out the unrelated and noisy features from the concatenated penultimate feature set  It also proves that the combined supervised and unsupervised neural networks, trained hierarchically, can learn the discriminative manifold better than the individual networks and it is crucial for improving the classification accuracy  In addition to accuracy, we also provide the kappa coefficients BIBREF24 of our method in Fig  FIGREF14   Here, a higher mean kappa value corresponding to a task implies that the network is able to find better discriminative information from the EEG data beyond random decisions  The maximum above-chance accuracy (75 92%) is recorded for presence/absence of the vowel task and the minimum (49 14%) is recorded for the INLINEFORM0  
To further investigate the feature representation achieved by our model, we plot T-distributed Stochastic Neighbor Embedding (tSNE) corresponding to INLINEFORM0 and V/C classification tasks in Fig  FIGREF8   We particularly select these two tasks as our model exhibits respectively minimum and maximum performance for these two  The tSNE visualization reveals that the second set of features are more easily separable than the first one, thereby giving a rationale for our performance 
Next, we provide performance comparison of the proposed approach with the baseline methods for PHASE-TWO of our study (cross-validation experiment) in Table TABREF15   Since the model encounters the unseen data of a new subject for testing, and given the high inter-subject variability of the EEG data, a reduction in the accuracy was expected  However, our network still managed to achieve an improvement of 18 91, 9 95, 67 15, 2 83 and 13 70 % over BIBREF17   Besides, our best model shows more reliability compared to previous works: The standard deviation of our model's classification accuracy across all the tasks is reduced from 22 59% BIBREF17 and 17 52% BIBREF18 to a mere 5 41% 
Conclusion and future direction
In an attempt to move a step towards understanding the speech information encoded in brain signals, we developed a novel mixed deep neural network scheme for a number of binary classification tasks from speech imagery EEG data  Unlike previous approaches which mostly deal with subject-dependent classification of EEG into discrete vowel or word labels, this work investigates a subject-invariant mapping of EEG data with different phonological categories, varying widely in terms of underlying articulator motions (eg: involvement or non-involvement of lips and velum, variation of tongue movements etc)  Our model takes an advantage of feature extraction capability of CNN, LSTM as well as the deep learning benefit of deep autoencoders  We took BIBREF17 , BIBREF18 as the baseline works investigating the same problem and compared our performance with theirs  Our proposed method highly outperforms the existing methods across all the five binary classification tasks by a large average margin of 22 51% 
Acknowledgments
This work was funded by the Natural Sciences and Engineering Research Council (NSERC) of Canada and Canadian Institutes for Health Research (CIHR) 

ground_truth: Viral tweets are the ones that are retweeted more than 1000 times

those that contain a high number of retweets