Currently, the highest spatial resolution can be reached by the Michigan
InfraRed Combiner-eXeter instrument (MIRC-X; Anugu et al., 2020) at the CHARA
Array (ten Brummelaar et al., 2005), which can provide an angular resolution
of about 0.4 mas. MIRC-X is also the only instrument that can combine light
from all the six 1-meter telescopes of the CHARA Array, making it the
instrument that is best suited for interferometric imaging of stellar
surfaces. Another facility that can be used for interferometric imaging is
European Southern Observatory’s Very Large Telescope Interferometer (VLTI).
The best angular resolution of the VLTI can be reached using the Precision
Integrated Optics Near Infrared ExpeRiment instrument (PIONIER; Le Bouquin et
al., 2011) that gives angular resolution of about 1.3 mas. VLTI can combine
four telescopes simultaneously and has changeable array configurations with
the 1.8-meter auxiliary telescopes. VLTI can also combine the light from the
four 8.2-meter Unit Telescopes telescopes, which gives the current faintest
limiting magnitudes for infra-red interferometry (GRAVITY instrument:
$K{=}10.5$ for on-axis observations and $K{=}17$ when using a close-by bright
fringe tracker star; Gravity Collaboration et al., 2017).

The number of targets that can be accurately mapped using interferometric
imaging is still limited to a handful of bright, nearby stars that have
angular diameters of about 2.0 mas or larger. The technique, however,
importantly opens a new parameter space that is not accessible using Doppler
or light-curve inversion imaging: cool, aged, main-sequence stars. Due to
their slow rotation, Doppler imaging cannot be used to image these stars.
Cool, nearby, main-sequence stars include some exoplanet hosts, like
$\epsilon$ Eridani.

New exciting discoveries are made with each new image obtained of stellar
surfaces. Future advances in interferometric imaging can be made by:
increasing the number of telescope that are combined to improve the image
fidelity, moving towards shorter wavelengths to obtain higher angular
resolution, and increasing the length of the longest baselines to obtain
higher angular resolution. Many of these aspects will be addressed, for
example, by the Visible Imaging System for Interferometric Observations
(VISION; Garcia et al., 2016) instrument at the upgraded Navy Precision
Optical Interferometer (NPOI; van Belle et al., 2020a) and the Stellar
Parameters and Images with a Cophased Array (SPICA; Pannetier et al., 2020) at
Center for High Angular Resolution Astronomy Array (CHARA Array; ten
Brummelaar et al., 2005).

Because the light detected with long-baseline optical interferometers must be
combined at the telescopes, there are physical limitations to the size of
ground-based interferometers. While efforts are being made to investigate
kilometer baselines (e.g., Monnier et al., 2018), space-based interferometers
(e.g., Monnier et al., 2018; van Belle et al., 2020b) could alleviate some of
the constraints on ground-based optical interferometers, allowing for
increased resolution and increased targets.

##### Empirical determination of fundamental stellar parameters.

As an added benefit, interferometry offers one of the most powerful, direct,
empirical means to determine the fundamental physical properties of stars.
Precise stellar parameters, including stellar radius, surface temperature, and
limb darkening, are crucial for correctly interpreting exoplanet observations.
While most stars cannot be resolved with current single-dish, ground- and
space-based telescopes, long-baseline optical and near-infrared
interferometers consisting of individual telescopes connected as an array can
provide accurate information on these parameters. When resolved stars are
observed with interferometry the measured squared visibility changes with the
baseline (distance between two telescopes) and wavelength. When limb-darkened
disk models are fit to the observed squared visibilities, the apparent stellar
diameter and limb-darkening profile can be determined. Additionally, the
effective temperature can be measured when the stellar diameter is combined
with observations of the bolometric flux.

Stellar diameters and limb-darkening laws have been determined
interferometrically for many stars, both giants and main sequence stars (e.g.,
Nordgren et al., 1999; Di Folco et al., 2004; Boyajian et al., 2012, 2013;
Mann et al., 2015). Diameter measurements have also been carried out for
exoplanet host stars (see, e.g., Baines et al., 2008, 2009; Bazot et al.,
2011; Crida et al., 2018; White et al., 2018). These observations have shown
the necessity of having sufficiently long baselines for accurately resolving
targets with small angular diameters. Additionally, one has to take great care
when selecting the calibration stars. Calibration stars are ideally nearby on
the sky, of similar spectral type, single, and unresolved by the
interferometer. Instrumentation developments that are ideally suited for
studying smaller exoplanet host stars include VISION at NPOI (Garcia et al.,
2016; van Belle et al., 2020a) and SPICA at the CHARA Array (ten Brummelaar et
al., 2005; Pannetier et al., 2020). With instruments working in the visible
wavelength range and with the long baselines of 432 m and 330 m, respectively,
NPOI and the CHARA Array are ideal facilities for studying fundamental stellar
parameters of the exoplanet host stars in the future.

Complementing these capabilities is the method of “pseudo-interferometry”
(Stassun et al., 2017, 2018), in which the stellar angular radius is
determined through an inversion of the Stefan-Boltzmann relation,
$\Theta=\left(F_{\rm bol}/\sigma_{\rm SB}T_{\rm eff}^{4}\right)^{1/2}$. The
physical radius then follows directly via the distance as determined, e.g.,
with a precise Gaia parallax. This approach does require an independent
determination of $T_{\rm eff}$, preferably via a high quality spectroscopic
analysis. As demonstrated by Stassun et al. (2017), the availability of
broadband apparent magnitudes spanning from the UV to the mid-IR allows
$F_{\rm bol}$ to be determined with a typical precision of a few percent, and
in the best cases $T_{\rm eff}$ determined to a precision of 1–2%, such that
angular radii for typical planet-host stars can be measured to 2–3 $\mu$m,
rivaling that of interferometric imaging for the brightest stars (though
without the benefit of resolving surface inhomogeneities, of course).
Moreover, as ultra-precise parallaxes are now routinely available with Gaia,
physical radii for typical stars via pseudo-interferometry can be determined
to better than a few percent (Stassun et al., 2017). Finally, such pseudo-
interferometric stellar radii can be further leveraged to measure the masses
of single stars empirically as well, if a precise surface gravity is available
such as through the technique of granulation “flicker” from a light curve
(Bastien et al., 2013, 2016). Masses of individual stars via this pseudo-
interferometric $+$ flicker gravity method can be determined to better than
10% in many cases and as good as a few percent in the best cases (Stassun et
al., 2018).

####  5.3.4 Our finding

From this analysis, we draw the following finding.

Finding 5.2

Summary: Many existing facilities can be leveraged to study exoplanet host
stars photometrically, though there is a need to scale observations to study a
large number of exoplanet host stars with long-term, multi-band photometry. UV
observations are also essential to inform models of host-star atmospheres, and
time-dependent measurements provide valuable information on active-region
coverage and temperature as well as flare frequency and magnitude. At the same
time, advances in interferometric technique enable constraints on photospheric
properties of nearby dwarfs, including some exoplanet hosts; of specific
interest is the direct mapping of temperature inhomogeneities on stellar
surfaces.

Capability Needed: Long-baseline, multi-band photometry of active exoplanet
host stars is needed to uniquely constrain active region temperatures and
sizes. Stellar modeling efforts would greatly benefit from a future
EUV/FUV/NUV dedicated mission with time-dependent measurements to improve our
understanding of active-region coverage and temperature, and flare frequency,
size, and color on these low-mass, planet-host stars. Much longer baseline
interferometric imaging capabilities on the ground and/or in space would allow
mapping of stellar surface inhomogeneities for a much larger sample of stars;
currently this can only be done for a handful of bright, nearby stars with
angular diameters larger than $\sim$2 mas.

Capability Today: Photometric studies of exoplanet host stars benefit from
many capable facilities, including dedicated transit-search networks and
longstanding contributions from amateur astronomers. The current state-of-the-
art for stellar atmosphere models is not at the same level as solar models due
to a dearth of stellar X-ray–NUV observational inputs.

Mitigation in Progress: The extended TESS mission continues to provide long-
baseline photometry in a broad red-optical bandpass for targets over nearly
the whole sky. The recently selected Pandora SmallSat Mission will provide
both long-baseline monitoring and transit observations of active exoplanet
host stars simultaneously in a visible photometric bandpass and a NIR
spectroscopic channel.

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∎ 11institutetext: Anusua Trivedi 22institutetext: Microsoft, 22email:
<EMAIL_ADDRESS>33institutetext: Alyssa Suhm 44institutetext: Microsoft,
44email<EMAIL_ADDRESS>55institutetext: Prathamesh Mahankal
66institutetext: University of Washington, 66email<EMAIL_ADDRESS>77institutetext: Subhiksha Mukuntharaj 88institutetext: University of
Washington, 88email<EMAIL_ADDRESS>99institutetext: Meghana D. Parab
1010institutetext: University of Washington, 1010email<EMAIL_ADDRESS>1111institutetext: Malvika Mohan 1212institutetext: University of Washington,
1212email<EMAIL_ADDRESS>1313institutetext: Meredith Berger
1414institutetext: Microsoft, 1414email<EMAIL_ADDRESS>1515institutetext: Arathi Sethumadhavan 1616institutetext: Microsoft,
1616email<EMAIL_ADDRESS>1717institutetext: Ashish Jaiman
1818institutetext: Microsoft, 1818email<EMAIL_ADDRESS>1919institutetext: Rahul Dodhia 2020institutetext: Microsoft, 2020email:
<EMAIL_ADDRESS>
# Defending Democracy: Using Deep Learning to Identify and Prevent
Misinformation

Anusua Trivedi *  — Alyssa Suhm  Prathamesh Mahankal  Subhiksha Mukuntharaj
Meghana D. Parab  Malvika Mohan  Meredith Berger  Arathi Sethumadhavan  Ashish
Jaiman  Rahul Dodhia

###### Abstract

The rise in online misinformation in recent years threatens democracies by
distorting authentic public discourse and causing confusion, fear, and even,
in extreme cases, violence. There is a need to understand the spread of false
content through online networks for developing interventions that disrupt
misinformation before it achieves virality. Using a Deep Bidirectional
Transformer for Language Understanding (BERT) and propagation graphs, this
study classifies and visualizes the spread of misinformation on a social media
network using publicly available Twitter data. The results confirm prior
research around user clusters and the virality of false content while
improving the precision of deep learning models for misinformation detection.
The study further demonstrates the suitability of BERT for providing a
scalable model for false information detection, which can contribute to the
development of more timely and accurate interventions to slow the spread of
misinformation in online environments.

###### Keywords:

democracy misinformation social media virality deep learning transformers

##  1 Introduction

A collective recognition of authoritative sources of credible information is
essential to the functioning of a democracy. People have access to more
information through the Internet than ever before, and nefarious actors use
this hyper-connected digital world to spread disinformation at unprecedented
speed and scale. From the individual to the societal level, misinformation has
real-world impacts, including increasing polarization, reinforcing societal
divisions, and even inciting violence. Misinformation distorts public
discourse and disrupts the authentic exchange of ideas and opinions.

Types of problematic information exist on a spectrum based on falseness and
harm RefE . Misinformation, while false, is not intended to deceive or cause
harm. In contrast, disinformation is false and created or distributed with the
intent to deceive or cause harm. The general motive to spread disinformation
is to damage an entity’s reputation, mislead the readers, or gain from
sensationalism. It is seen as one of the greatest threats to democracy, open
debate, and free and modern society RefL .

Despite these distinctions in taxonomy, some authors use “misinformation” as
an umbrella term to refer to all types of false information. Brennen et al.
RefF  states that they use “misinformation” to refer broadly to any false
information because it is hard to assess if false content was created to
deceive or by mistake. They focus on false information, including both
misinformation and disinformation. This paper follows this precedent in using
“misinformation,” as the datasets used to train the model focus only on
whether the content is false, regardless of intent.

Prior research confirms that Americans across the political spectrum are
concerned about false information RefA . According to Pew Research Center, 63%
of Americans surveyed report that they do not trust the news they encounter on
social media, despite 67% of respondents also acknowledging that they use
social media to get news on a regular basis RefB . Trust in information is
shaped by social norms and individual belief systems, and once false
information gains acceptance, it can be highly resistant to correction RefB1 .
Additionally, studies show that false information travels significantly
farther, faster, deeper, and more broadly than credible information on social
media platforms RefC . Previous authors have hypothesized that this may be due
to the novelty of false information and the emotional response it often
evokes, such as surprise, fear, or disgust RefC . Current responses to
misinformation on social media platforms are often reactive, taking place
after the content has been distributed widely and after the damage has already
largely been done. Unfortunately, even when widely discredited, exposure to
misinformation can create lingering effects on peoples’ attitudes and
perceptions RefD .

Misinformation is increasingly being shared via social media platforms like
Twitter, YouTube, and Facebook RefK . These platforms offer the general
population a forum to share their opinions and views in a raw and unedited
fashion. Some news articles hosted or shared on social media platforms have
more views than direct views from the media outlets’ platforms. Researchers
studied the velocity of misinformation and concluded that tweets containing
false information reach people on Twitter six times faster than truthful
tweets RefL .

There is a need better to understand the spread of false content through
online networks to develop interventions that disrupt misinformation before it
achieves virality. According to Granik et al. RefG , artificial intelligence
performs better to detect misinformation. In this paper, we utilize a deep
learning model to predict if the content is false. We also use visualizations
of how that content spreads through social media over time as an approach to
detecting the virality of false content.

##  2 Prior Work

Detecting misinformation is one of the most challenging tasks for a human
being. Researchers have explored supervised machine learning models to detect
misinformation. Abdullah-All-Tanvir et al. RefH  used SVM and Naïve Bayes
classifiers for detecting false content. Rahman et al. RefI  found that the
Logistic regression is a better classifier model. According to Ahmed et al.
RefJ , false information has a significant impact on the political situation
of society. They use a Naïve Bayes classifier to detect false information from
Facebook posts. Most of this work is based on different handheld features,
which can capture linguistic and psychological causes. However, these features
failed to classify text well, which limits the generalized performance of the
models. Thus researchers started exploring neural network-based models that
learn document-level representation to discover deception text. Neural network
models have been shown to learn semantic representations for NLP tasks
successfully. Bambu et al. RefM  have recently been able to show the use of
graph-based methods and deep learning approaches like CNN in combating false
information on social media. In this paper, we focus on the application of
Deep Bidirectional Transformers for Language Understanding (BERT) RefN  to the
problem of misinformation detection.

Figure 1: BERT binary classification model for Misinformation Detection

Figure 2: Example of a propagation graph as described in RefV1

Figure 3: Using binary misinformation classification result to detect virality
trend of hashtags

Figure 4: Precision-Recall curve and F1-Score of our model on validation
dataset

##  3 Data

As an initial stage towards building an automated solution for identifying
misinformation, we curated datasets from different open source labeled
databases curated by experts. The dataset used to train the machine learning
model was created by merging text content (independent variable) and fake/real
labels (dependent variable) from different public data sources. Below is the
list of the data sources used:

  * •

LIAR: LIAR RefN1  is a publicly available dataset for fake news detection. The
LIAR dataset consists of 12.8K human-labeled short statements from
Politifact.com’s API, and a Politifact.com editor for its truthfulness
evaluates each statement. This dataset can be used for fact-checking as well.
Notably, this new dataset is an order of magnitude larger than the previously
largest public fake news datasets of similar type.

  * •

ISOT Fake News: The ISOT Fake News dataset RefN2  is a compilation of several
thousand fake news and truthful articles obtained from different legitimate
news sites and sites flagged as unreliable by Politifact.com.

  * •

Horne 2017 Fake News Data: Introduced by Horne et al. RefN3 , this dataset
consists of two independent news datasets:

Buzzfeed Political News Data: This news dataset is analyzed by Craig Silverman
et al. RefN4 , where they use the keyword search on the content analysis tool
“BuzzSumo” to find news stories and post the analysis. The authors collect the
body text and body title of all articles and use the ground truth set by
Buzzfeed as the actual ground truth. This dataset has fewer clear restrictions
on the ground truth, including opinion-based real stories and satire-based
fake stories. We manually filter this data to contain only “hard” news stories
and “malicious” fake news stories for our paper.

Random Political News Data: This consists of randomly collected news from
Business Insider’s “Most Trusted” list and Zimdars 2016 Fake news list. The
“Real” news came from Wall Street Journal, the Economist, BBC, NPR, ABC, CBS,
USA Today, the Guardian, NBC, and the Washington Post. The “Satire” news came
from The Onion, Huffington Post Satire, Borowitz Report, the Beaverton, Satire
Wire, and Faking News. The “Fake” news came from Ending The Fed, True Pundit,
DC Gazette, Liberty Writers News, Before its News, InfoWars, Real News Right
Now. Our paper uses only the “Real” and “Fake” labels and text from this
dataset.

  * •

Russian Troll tweets: This dataset is compiled and published by Linvill et al.
RefN5 . As part of the House Intelligence Committee investigation into how
Russia may have influenced the 2016 US Election, authors released almost 3000
Twitter accounts believed to be connected to Russia’s Internet Research
Agency, a company known for operating social media troll accounts.

  * •

FakeNewsNet: Shu et al. RefN6  constructed and publicized a multi-dimensional
data repository, which contains two datasets with news content, social media
context, and spatiotemporal information. The dataset is constructed using an
end-to-end FakeNewsTracker RefN7  system.

  * •

UTK Machine Learning Club: UTK Machine Learning Club curated a dataset to
identify if an article might be fake news RefN8 .

  * •

Kaggle Fake News Detection: Kaggle user Jruvika curated a dataset of fake news
and open-sourced it for public use RefN9 .

  * •

NBC Election Troll Tweets 2016: This data set consists of 11.5k expanded news
links in set of 36.5k 2016 election troll tweets RefN10 .

  * •

Fake News on Twitter 2016 viral tweets: Longa et al. RefN11  curated a
collection of tweets that went viral on 2016 US election day (up until March
2017). Viral tweets are those that achieved the 1000-retweet threshold during
the collection period. We queried Twitter’s streaming API using the hashtags
#MyVote 2016, #ElectionDay, #electionnight, and the user handles
@realDonaldTrump and @HillaryClinton.

The final combined dataset has 98532 text content and binary ‘Fake/Real’
(1=fake, 0=real) label pairs. All the above datasets were transformed to make
labels binary wherever needed. For example, we labeled the rows as ‘Real’ for
the LIAR dataset if the content was labeled ‘true’ or ‘mostly-true’ and ‘Fake’
if the content was labeled ‘pants-on-fire’ or ‘false’. Content labeled ‘half-
true’ or ‘barely-true’ was not included.

##  4 Method

Text Classification is a long-standing problem in NLP, and the community has
introduced several paradigms that differ from each other in type and approach.
In this paper, we are particularly interested in the context-aware
misinformation classification paradigm. The label for each text snippet can be
obtained by referring to its accompanying context (paragraph or a list of
sentences). In this paper, we approach the problem in two steps:

  * •

Build a transformer-based classifier for real and false information
prediction.

  * •

Use this prediction model to extract features from the propagation graphs to
further analyze how misinformation propagates on social media (e.g. Twitter).

###  4.1 Fine-tuning BERT for binary misinformation classification

Research in the field of using pre-trained deep learning models has resulted
in a massive leap in state-of-the-art results for text classification. ELMo
RefO , ULMFiT RefP  and OpenAI Transformer RefQ  are some of the popular
models which have shown immense success in the text classification task. All
these models allow us to pre-train an unsupervised language model on a large
corpus of data such as all Wikipedia articles and then fine-tune these pre-
trained models on downstream tasks. More recent methods mostly employ the
Transformer architecture RefQ1  as their base model design, which does not
process sequentially like RNNs and instead relies entirely on the attention
mechanism, e.g., BERT RefN , GPT-2 RefQ2 , XLNet RefQ3 , MegatronLM RefQ4 ,
and Turing-NLG RefQ5 . Transformer models have complex architecture consisting
of multiple parts such as embedding layers, self-attention layers, and feed-
forward layers. These are powerful models with an improved state of the art
for different NLP tasks by significant margins.

Modern deep neural networks often significantly benefit from transfer
learning. Deep convolutional neural network (CNN) RefR  trained on a large
image classification dataset such as ImageNet RefS  have proved to help
initialize models on other computer vision tasks RefT . Transfer learning for
a new purpose-specific model, using the pre-trained neural network as the
basis, has shown immense success. In recent years, similar transfer learning
techniques have been useful for many natural language tasks. We use the
smaller BERT-Base-uncased model as the base model for this paper. The BERT-
Base-uncased model has 12 attention layers and uses the word-piece-tokenizer
RefU , which converts all text to lowercase. We modify the Bert-For-Sequence-
Classification class in BERT GitHub RefV  for multi-label classification. We
use binary-cross-entropy-with-logits RefW  as loss function for binary
classification task instead of the standard cross-entropy loss used by the
BERT model. Binary-cross-entropy loss allows our model to assign independent
probabilities to the labels. Figure 1 explains our multilabel classification
pipeline. The training logic is identical to the one provided in classifier.py
in RefV . We train the model for ten epochs with a batch size of 32 and
sequence length as 256. The learning rate is kept to 3e-5, as recommended for
fine-tuning in the original BERT paper. We do not use the precision FP16
technique as the binary-cross-entropy-with-logits loss function does not
support FP16 processing.

###  4.2 Using binary misinformation classification model to create
Propagation Graphs in Twitter

As described by Marion et al. RefV1 , propagation graphs are derived from the
set of tweets and re-tweets corresponding to a piece of information (e.g.
hashtag). Let’s define that set as T. A node can be of two types -

  * •

A tweet node: The node stores the tweet and its associated user. A tweet
belongs to a news graph if it contains the keywords extracted from the news
article’s headline.

  * •

A re-tweet node: The node stores the re-tweet and its associated user. All re-
tweets of a tweet node are present in the graph.

Edges are drawn between a tweet and its re-tweets. Edges contain a time weight
that corresponds to the time difference between the tweet and re-tweet publish
times. Let the set of edges of the graph be E.

A news graph comprises non-connected sub-graphs where each sub-graph comprises
a tweet and its associated re-tweets. Then we can define a news graph as N =
(T,E).

It is important to note that Twitter is designed in such a way that a re-tweet
of a re-tweet will point back to the original tweet. Hence, the depth of the
graph is never more than 1. Figure 2 shows an example of a propagation graph.

We create a Twitter Propagation graph to enhance the visibility and symbolic
power of social media information. Here we try to examine how characteristics
of hashtags drove the misinformation virality during a social networking
event. We extract data pertinent to a hashtag, send it to our machine learning
model for misinformation detection, and then users are clustered together with
the help of community detection algorithms from Louvain Refa  and InfoMap Refb
. The whole architecture is defined in Figure 3. Through propagation graphs,
we can identify popular hashtag types and examined hashtag co-occurrence
patterns. The graph visualization of this clustered output helps us detect re-
tweets and track hashtag virality of misinformation.

##  5 Evaluation of our model

###  5.1 Classifier Outcome

Out of the 98,532 text content, we used 80% for training and 20% for
evaluating the model. The validation dataset consists of 19,706 records and
the evaluation of our model on that held-out dataset had an F1 score of 84.43%
(Precision = 0.90, Recall = 0.89, Mathews Corr Coef = 0.44). Figure 4 shows
the precision-recall curve and confusion matrix on our validation dataset.

Figure 5: Tweet retweet interaction network graph. Here the node size is
scaled according to out degree score. The nodes have been recolored to reflect
fake and real tweets.

Figure 6: Hashtag network graph for tweets collected with hashtag #covid19.
The graph shows the frequency of occurrence of two hashtags in the same tweet.
The size of the hashtag shows the number of times it has been used.

###  5.2 Propagation Graph Analysis

The Propagation Graph analysis for a particular hashtag is shown in Figure 5
and Figure 6. Users have been clustered together with the help of community
detection algorithms from Louvain Refa  and InfoMap Refb . The tweet-retweet
interaction graph in Figure 5 shows nodes as users and links between nodes if
a user has re-tweeted. Users who have re-tweeted are placed closer to each
other due to the force-directed layout. In Figure 6, each node of the hashtag
network is a hashtag used by a user. A link is present between hashtags if
they appear in the same tweet. Hashtag text size depicts how often it is used.
These graph visualizations give more context about what the users are
interacting with. These are also spread out in a force-directed layout.

For both the graphs, we can obtain information about the number of nodes,
links, user information, first and last date of the tweet. Based on the number
of followers, the node size can be changed. This helps to show how influential
the user sharing real or false information could be. The code for both these
visualizations is written partly by the visualizer in Twitter explorer Refc .
Python implementation of the igraph library Refd  is used for making the graph
networks. D3.js Refe  is also used for creating the interactive networks and
force-graph.

##  6 Discussion

BERT misinformation classification provides a significant step forward in
applying deep learning to detecting and slowing the spread of false
information online. The results confirm prior work indicating that
misinformation content on a particular topic typically originates from a small
number of core users. Further, temporal information on trending topics
indicates that virality is usually achieved within several hours to days.
These initial findings suggest that BERT may perform with a higher level of
precision than prior methods, contributing to the continued refinement of AI-
enabled misinformation detection. However, subsequent testing is needed to
evaluate the model’s performance on unfamiliar datasets.

This research contributes to a better academic understanding of the spread of
information online and the development of better technological interventions
to counter misinformation. Interventions such as outright content removal or
user account takedown are controversial and challenging to enforce equitably,
especially given that the window of time to respond to a viral misinformation
campaign is narrow. Therefore, these results and future research could help
better inform other response options, including algorithmic interventions or
labeling and contextualization.

Future research should seek to apply this approach to data from other social
media platforms and misinformation content in non-English languages. An
additional challenge requiring further study is classifying information that
is partially true but mischaracterized or mis-contextualized. Finally, as
those trying to detect misinformation will always face an asymmetric
disadvantage to those trying to spread it, future technology solutions will
likely shift towards media authentication and provenance tools that enable
verification at the point of origin and throughout the lifecycle of a piece of
content.

Misinformation threatens authentic democratic discourse and the shared
conceptualization of reality that underpins healthy societies. While upholding
freedom of speech and expression, it is also important to maintain an
individual’s right to access accurate, truthful information. Identifying and
mapping the spread of misinformation through online networks will help those
seeking to curb its impact develop more accurate, equitable, and transparent
interventions.

## Declarations

### Funding: Not applicable

### Conflict of interest: The authors declare that they have no conflict of
interest.

### Availability of data and material: All data is open source.

### Code availability: https://github.com/prathameshmahankal/Fake-News-
Detection-Using-BERT

### Ethics approval: Not applicable

### Consent to participate: The authors declare that they consent to
participate.

### Consent for publication: The authors declare that they consent to
publication.

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