=Paper=
{{Paper
|id=Vol-2657/short2
|storemode=property
|title=Depressive, Drug Abusive, or Informative: Knowledge-aware Study of News Exposure during COVID-19 Outbreak
|pdfUrl=https://ceur-ws.org/Vol-2657/short2.pdf
|volume=Vol-2657
|authors=Amanuel Alambo,Manas Gaur,Krishnaprasad Thirunarayan
|dblpUrl=https://dblp.org/rec/conf/kdd/AlamboGT20
}}
==Depressive, Drug Abusive, or Informative: Knowledge-aware Study of News Exposure during COVID-19 Outbreak==
https://ceur-ws.org/Vol-2657/short2.pdf
Depressive, Drug Abusive, or Informative:
Knowledge-aware Study of News Exposure during COVID-19
Outbreak
Amanuel Alambo Manas Gaur Krishnaprasad Thirunarayan
Knoesis Center AI Institute, University of South Knoesis Center
Dayton, Ohio Carolina Dayton, Ohio
amanuel@knoesis.org Columbia, South Carolina tkprasad@knoesis.org
mgaur@email.sc.edu
ABSTRACT on Knowledge-infused Mining and Learning (KiML’20). , 5 pages. https://doi.
The COVID-19 pandemic is having a serious adverse impact on org/10.1145/nnnnnnn.nnnnnnn
the lives of people across the world. COVID-19 has exacerbated
community-wide depression, and has led to increased drug abuse
brought about by isolation of individuals as a result of lockdown.
1 INTRODUCTION
Further, apart from providing informative content to the public,
the incessant media coverage of COVID-19 crisis in terms of news COVID-19 pandemic has changed our societal dynamics in different
broadcasts, published articles and sharing of information on social ways due to the varying impact of the news articles and broadcasts
media have had the undesired snowballing effect on stress levels on a diverse population in the society. Thus, it is important to
(further elevating depression and drug use) due to uncertain future. place the news articles in their spatio-temporal-thematic (Nagarajan
In this position paper, we propose a novel framework for assessing et al., 2009; Andrienko et al., 2013; Harbelot et al., 2015) contexts to
the spatio-temporal-thematic progression of depression, drug abuse, offer appropriate and timely response and intervention. In order
and informativeness of the underlying news content across the to limit the scope of this research agenda, we propose to focus
different states in the United States. Our framework employs an on identifying regions that are exposed to depressive and drug
attention-based transfer learning technique to apply knowledge abusive news articles and to determine/recommend ways for timely
learned on a social media domain to a target domain of media interventions by epidemiologists.
exposure. To extract news articles that are related to COVID-19 The impact of COVID-19 on mental health has been investigated
communications from the streaming news content on the web, we in recent studies (Garfin et al., 2020; Holmes et al., 2020; Qiu et al.,
use neural semantic parsing, and background knowledge bases in a 2020). [4] studied the impact of repeated media exposure on the men-
sequence of steps called semantic filtering. We achieve promising tal well-being of individuals and its ripple effects. [8] underscore
preliminary results on three variations of Bidirectional Encoder the importance of a multidisciplinary study to better understand
Representations from Transformers (BERT) model. We compare COVID-19. Specifically, the study explores its psychological, social,
our findings against a report from Mental Health America and the and neuroscientific impacts. [12] studied the psychological impact
results show that our fine-tuned BERT models perform better than COVID-19 lockdown had on the Chinese population. These studies,
vanilla BERT. Our study can benefit epidemiologists by offering however, do not adequately explore a technique to computationally
actionable insights on COVID-19 and its regional impact. Further, analyze the regional repercussions associated with media exposure
our solution can be integrated into end-user applications to tailor to COVID-19 that may provide a better basis for local grassroots
news for users based on their emotional tone measured on the scale level action.
of depressiveness, drug abusiveness, and informativeness. We propose an approach to measure depressiveness, drug abu-
siveness, and informativeness as a result of media exposure for
various states in the US in the months from January 2020 to March
KEYWORDS
2020. Our study is focused on the first quarter of 2020 as this period
COVID-19; Spatio-Temporal-Thematic; Depressiveness; Drug was critical in the spread of COVID-19 and its ominous impact;
Abuse; Informativeness; Transfer Learning this was a period when the public faced major changes to lifestyle
ACM Reference Format: including lockdown, social distancing, closure of businesses, unem-
Amanuel Alambo, Manas Gaur, and Krishnaprasad Thirunarayan. 2020. ployment, and broadly speaking, complete lack of control over the
Depressive, Drug Abusive, or Informative: Knowledge-aware Study of News unfolding situation precipitating in severe uncertainty about the
Exposure during COVID-19 Outbreak . In Proceedings of KDD Workshop impending future. In consequence, this continued media exposure
progressively worsened the mental health of individuals across the
board. We analyze and score news content on three orthogonal
In M. Gaur, A. Jaimes, F. Ozcan, S. Shah, A. Sheth, B. Srivastava, Proceedings of the
Workshop on Knowledge-infused Mining and Learning (KDD-KiML 2020). San Diego, dimensions: spatial, temporal, and thematic. For spatial, we use
California, USA, August 24, 2020. Use permitted under Creative Commons License state boundaries. For temporal, we use monthly data analysis. For
Attribution 4.0 International (CC BY 4.0).
thematic, we score news content on the category/dimension of
KiML’20, August 24, 2020, San Diego, California, USA,
© 2020 Copyright held by the author(s). depression, drug abuse and informativeness (relevant to COVID-19
https://doi.org/10.1145/nnnnnnn.nnnnnnn but not directly connected to either depression or drug-abuse).
and grouped the ones that are from the US based on their state
of origination. The state-level grouped news articles had a total
of over 150K entities identified using DBpedia spotlight service2 .
However, since using a coarse filtering service such as DBpedia
spotlight over the entire news articles is not efficient and brings
in irrelevant entities, and thus noisy news articles, we utilize (“i”)
a neural parsing approach with self-attention (Wu et al., 2019) to
extract relevant entities. After extracting relevant entities and news
articles, we use (“ii”) DBpedia spotlight service to identify news
articles that are related to online communications about COVID-19.
Figure 1: Spatio-Temporal-Thematic Dimensions
Figure 2: Knowledge-based entity extraction using Semantic
Filtering
Our study hinges on the use of domain-specific language model-
ing and transfer learning to better understand how depressiveness, For this task, we explored 780 DBpedia categories that are rel-
drug abusiveness, and informativeness of news articles evolve in evant to COVID-19 communications to create the most relevant
response to media exposure by people. We conduct the transfer set of entities and news articles. Further, upon inspection of the
of knowledge learned on a social media platform to the domain news articles, we discovered medical terms that were not available
of exposure to news using variations of the attention-based BERT in DBpedia. As a result, we used (“iii”) the MeSH terms hierarchy
model (Devlin et al., 2018), also called Vanilla BERT. Thus, in addi- in Unified Medical Language System (UMLS), the Diagnostic and
tion to vanilla BERT, we fine-tune BERT models on corpora that Statistical Manual for Mental Disorders (DSM-5) lexicon (Gaur et al.,
are representative of depression and drug abuse. Then, we compare 2018), and Drug Abuse Ontology (DAO), collectively referred to
results obtained using the three variants of the BERT model. For as Mental Health and Drug Abuse Knowledgebase (MHDA-Kb) to
scoring depressiveness, drug abusiveness, and informativeness of spot additional entities. Thus, from 700K unique news articles
news articles, we utilize entities from structured domain knowledge (which are extracted from the total of 1.2 Million news articles by
from the Patient Health Questionnaire (PHQ-9) lexicon (Yazdavar removing duplicates), we created a set of 120K unique entities that
et al., 2017), Drug Abuse Ontology (DAO) (Cameron et al., 2013), are described by the 780 DBpedia categories and 225 concepts in
and DBpedia (Lehmann et al., 2015). PHQ-9 lexicon is a knowl- MHDA-Kb. The figures below show two examples that illustrate
edge base developed specifically for assessing depression, and DAO entities spotted during entity extraction on a sample news article.
is built to study drug abuse. Similarly, we use DBpedia, which is A news article that has entities identified using this sequence of
a generic and comprehensive knowledge base, for assessing the steps is selected for our study.
informativeness of news content.
Having determined the scores for depressiveness, drug abusive-
ness, and informativeness of news articles for each state during
the three months, we computed the aggregate score for each the-
matic category by summing up the scores for the news articles. We
finally assigned the category with the highest score as a label for
a state. For instance, if the aggregate score of depressiveness for
the state of Iowa in the month of January 2020 is the highest of
the three thematic categories, then the state of Iowa is assigned a Figure 3: Example entity extraction-I using Semantic Filter-
label of depression for that month, which means the state of Iowa ing
is most exposed to depressive news contents. Thus, identifying
which states are consistently exposed to depressive or drug abusive
news contents enables policy makers and epidemiologists to devise
appropriate intervention strategies.
2 DATA COLLECTION
We collected 1.2 Million news articles from the Web and GDELT1 (a
resource that stores world news on significant events from different
countries) using semantic filtering (Sheth and Kapanipathi, 2016) Figure 4: Example entity extraction-II using Semantic Filter-
and spanning the period from January 01, 2020, to March 29, 2020. ing
We filtered news articles that did not originate from within the US
1 https://www.gdeltproject.org/ 2 https://www.dbpedia-spotlight.org/
2
3 METHODS scores of news articles as described. The category with the highest
We propose to use three variations of the BERT model for represent- cumulative score is set as the label for a state.
ing news articles. In its basic form, we use vanilla BERT for encoding Using vanilla-BERT (Figure 5), we can see that no state shows
news articles. For the remaining two variations, we fine-tune BERT exposure to news content on drug abuse in January. Going from
on a binary sequence classification task by independently training February to March, we see depressive news content move from
on two corpora using masked language modeling (MLM) and next inner-most states such as Missouri, Kansas, and Colorado to border
sentence prediction (NSP) objectives. The two corpora used are: 1) states such as California, Montana, North Dakota, and Louisiana,
Subreddit Depression (Gkotsis et al., 2017; Gaur et al., 2018); 2) A making way for informative news content. Further, there are fewer
combination of subreddits: Crippling Alcoholism, Opiates, Opiates states exposed to drug-related news content than those exposed
Recovery, and Addiction (abbreviated COOA), each consisting of to depressive or informative news content in February or March.
Reddit posts about drug abuse. Subreddit Depression has 760049 Particularly, Arizona and Virginia show consistent exposure to
posts across 121795 Redditors, and COOA has 1416765 posts from drug-related news content in February and March.
46183 users, both consisting of posts from the years 2005 - 2016. Using depression-BERT, as shown in Figure 6, we see that states
Reddit posts belonging to subreddits depression or COOA are con- such as Texas, and Kansas are exposed to depressive news content
sidered positive classes and the 380444 posts from control group for the month of January and February while states such as Cali-
(∼10K subreddits unrelated to mental health) as negative classes. fornia, Montana, Alaska, and Michigan show higher consumption
We use the following settings for training our BERT model for se- of depressive news content in February and March. With regard to
quence classification: training batch size of 16, maximum sequence informativeness, we see an overall even distribution of informative
length of 256, Adam optimizer with learning rate of 2e-5, number of news content across the nation in February and March. Further,
training epochs set to 10, and a warmup proportion of 0.1. We used we see a few midwest states showing relatively higher instances of
40%-60% split for training and testing sets for creating the BERT news content that are informative than depressive in February and
models and achieved a test accuracy of 89% for Depression-BERT March. It’s interesting to see a few southern states such as Okla-
and 78% for Drug Abuse-BERT. We set the size of the training set homa, Texas, and Arkansas transition from exposure to depressive
smaller than the testing set for generalizability of our models. In news content in the month of February to drug use related news
this manuscript, we refer to the BERT model fine tuned on subreddit content in the month of March.
depression as Depression-BERT or DPR-BERT, while the one fine Using Drug Abuse-BERT model (Figure 7), states such as Texas,
tuned on subreddit COOA as Drug Abuse-BERT or DA-BERT. and Wisconsin shift from exposure of depressive news content in
In addition to using BERT for encoding news contents, we also January to exposure of drug-related news content in February, while
use it for representing the entities in the background knowledge states such as California, and Oklahoma transition from exposure to
bases (i.e., PHQ-9, DAO, and DBpedia). Once we have encoded the depressive news content in February to drug-related news content
news articles and the entities in the knowledge bases using vanilla in March. Further, we see the informativeness of news content
BERT or fine-tuned BERT model, we generated depressiveness sweeping from the east to the midwest, to parts of the south, and
score, drug abusiveness score, and informativeness score corre- to some parts of the west from February to March.
sponding to the entities in PHQ-9, DAO, and DBpedia respectively. Our results show that a fine-tuned BERT model cleanly separates
The equation below gives the score of a news article for a category the thematic categorical scores to a state. For instance, using DA-
given one of the BERT models: BERT for the month of March, the drug abuse score for the state
of California is much higher than the score of depressiveness or
informativeness for the same state. However, with the vanilla BERT
|E𝐾𝐵 |
1 Õ model, the three scores computed for the various states and months
𝑆𝑐𝑜𝑟𝑒𝑐𝑚 (𝑛𝑒𝑤𝑠) = 𝑐𝑜𝑠𝑠𝑖𝑚 (news, 𝑒) (1) are marginally different. Moreover, the results using DPR-BERT or
|E𝐾𝐵 | 𝑒=1
DA-BERT capture the state-level ranking of mental disorders by
Mental Health America 3 better than vanilla-BERT; for a few states,
where,
the fine-tuned BERT models identify more months to have media
m ∈ {vanilla-BERT, DPR-BERT, DA-BERT}
exposure to depression or drug abuse news content.
c ∈ {informativeness, depressiveness, drug abuse}
cossim (news, e): cosine similarity between a news content and As indicated in Table 1, we report months showing predominant
an entity in KB media exposure to either depressive or drug abuse news articles
KB - a collection of entities present in PHQ-9, DBpedia, or DAO using the three variants of BERT model. We use 10 of the 13 states
recognized as showing high prevalence of mental disorders accord-
We used the base variant of the BERT model with 12 layers, 768 ing to a report by Mental Health America on overall mental disorder
hidden units, and 12 attention heads. We use PyTorch 1.5.0+cu101 ranking. The 3 states not included in this table are Washington,
for fine-tuning our BERT models. All our programs were run on Wyoming, and Idaho. We did not consider these 3 states as these
Google Colab’s NVIDIA Tesla P100 PCI-E GPU. states were not in our dataset cohort. For the Mental Health Amer-
ica (MHA) report, we make a practical assumption that each of the
4 PRELIMINARY RESULTS AND DISCUSSION three months is either depressive or drug abusive for each state.
Thus, our objective is to maximize the number of months with
In this section, we report the state-wise labels (i.e., depressive, drug
abusive, informative) for each month obtained after summing the 3 https://www.mhanational.org/issues/ranking-states
3
Figure 5: vanilla BERT modeling of Depressiveness, Drug Abuse, and Informativeness in US states.
Figure 6: Depression-BERT (DPR-BERT) modeling of Depressiveness, Drug Abuse, and Informativeness in US states
Figure 7: Drug Abuse BERT (DA-BERT) modeling of Depressiveness, Drug Abuse, and Informativeness in US states
exposure to depressive/drug abuse news content for each of the where,
10 states. We can see in Table 1 that fine-tuned BERT models help 𝑚 1, 𝑚 2 ∈ {vanilla-BERT, DPR-BERT, DA-BERT, MHA}
identify more months to having exposure to depressive or drug 𝑆 - Set of States in the US (Table 1)
abuse news content than vanilla BERT does for the 10 states. For ex- 𝑚𝑀 𝑀
1 , 𝑚 2 : Number of depressive, drug abusive, or informative
ample, using DA-BERT, five states are identified to have at least two months for a state “i”
months showing exposure to depressive/drug abuse news content We report inter-model and model-to-MHA Jaccard similarity
while DPR-BERT identifies six states to having been exposed to scores computed using equation (2) in Figure 8.
depressive/drug abuse news content for two months. On the other As shown in Figure 8, DA-BERT gives the best results against
hand, vanilla-BERT identifies only two states with depressive/drug MHA report in Jaccard similarity (0.53), which means DA-BERT
abuse news content for two months. To compare models with one identifies over half of the state-to-month instances in MHA. On the
another and against the report by Mental Health America (MHA), other hand, vanilla-BERT has a Jaccard similarity of 0.37 with MHA,
we compute a Jaccard Index between each pair of models and each which can be interpreted as vanilla-BERT identifies a little over
model against the report from MHA. The equation below computes one-third of the state-to-month instances in MHA. The best Jaccard
Jaccard similarity between the results of two models or a model’s similarity is achieved between DPR-BERT and vanilla-BERT (0.7);
results with an MHA report. thus, 70% of state-to-month mappings are shared between DPR-
BERT and vanilla-BERT based on Jaccard index. It’s interesting to
|𝑆 | see DA-BERT has the same Jaccard similarity with vanilla-BERT
Õ 𝑚𝑀 ∩ 𝑚𝑀
1 2
𝐽 (𝑚 1, 𝑚 2 ) = 𝑀 𝑀
(2)
𝑖 ∈ 𝑆 𝑚1 ∪ 𝑚2
4
MHA States vanilla- DA-BERT DPR-BERT from Mental Health America. In the future, we plan to incorporate
with high BERT (Months (Months background knowledge bases in our attention-based transfer learn-
DPR and DA (Months with depres- with ing framework to further investigate knowledge-infused learning
with depres- sion/drug depres- (Kursuncu et al., 2019).
sion/drug abuse) sion/drug
abuse) abuse) REFERENCES
[1] Gennady Andrienko, Natalia Andrienko, Harald Bosch, Thomas Ertl, Georg Fuchs,
Tennessee Feb, Mar Feb, Mar Feb, Mar Piotr Jankowski, and Dennis Thom. 2013. Thematic patterns in georeferenced
Alabama Feb Feb, Mar Feb tweets through space-time visual analytics. Computing in Science & Engineering
15, 3 (2013), 72–82.
Oklahoma Mar Feb, Mar Feb, Mar [2] Delroy Cameron, Gary A Smith, Raminta Daniulaityte, Amit P Sheth, Drashti
Kansas Feb Jan, Feb Jan, Feb Dave, Lu Chen, Gaurish Anand, Robert Carlson, Kera Z Watkins, and Russel
Falck. 2013. PREDOSE: a semantic web platform for drug abuse epidemiology
Montana Mar Feb Feb, Mar using social media. Journal of biomedical informatics 46, 6 (2013), 985–997.
South Carolina Mar Mar Feb, Mar [3] Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018. Bert:
Alaska Feb, Mar Jan, Feb, Mar Feb, Mar Pre-training of deep bidirectional transformers for language understanding. arXiv
preprint arXiv:1810.04805 (2018).
Utah Mar Mar Mar [4] Dana Rose Garfin, Roxane Cohen Silver, and E Alison Holman. 2020. The novel
Oregon None Feb None coronavirus (COVID-2019) outbreak: Amplification of public health consequences
Nevada Feb Feb None by media exposure. Health psychology (2020).
[5] Manas Gaur, Ugur Kursuncu, Amanuel Alambo, Amit Sheth, Raminta Daniu-
Table 1: Evaluation of base and domain-specific BERT mod- laityte, Krishnaprasad Thirunarayan, and Jyotishman Pathak. 2018. " Let Me Tell
You About Your Mental Health!" Contextualized Classification of Reddit Posts to
els for MHA states over the period of three months (January, DSM-5 for Web-based Intervention. In Proceedings of the 27th ACM International
February, and March). These three months showed high dy- Conference on Information and Knowledge Management. 753–762.
[6] George Gkotsis, Anika Oellrich, Sumithra Velupillai, Maria Liakata, Tim JP Hub-
namicity in COVID-19 spread. bard, Richard JB Dobson, and Rina Dutta. 2017. Characterisation of mental health
conditions in social media using Informed Deep Learning. Scientific reports 7
(2017), 45141.
[7] Benjamin Harbelot, Helbert Arenas, and Christophe Cruz. 2015. LC3: A spatio-
temporal and semantic model for knowledge discovery from geospatial datasets.
Journal of Web Semantics 35 (2015), 3–24.
[8] Emily A Holmes, Rory C O’Connor, V Hugh Perry, Irene Tracey, Simon Wes-
sely, Louise Arseneault, Clive Ballard, Helen Christensen, Roxane Cohen Silver,
Ian Everall, et al. 2020. Multidisciplinary research priorities for the COVID-19
pandemic: a call for action for mental health science. The Lancet Psychiatry
(2020).
[9] Ugur Kursuncu, Manas Gaur, and Amit Sheth. 2019. Knowledge Infused Learning
(K-IL): Towards Deep Incorporation of Knowledge in Deep Learning. arXiv
preprint arXiv:1912.00512 (2019).
[10] Jens Lehmann, Robert Isele, Max Jakob, Anja Jentzsch, Dimitris Kontokostas,
Pablo N Mendes, Sebastian Hellmann, Mohamed Morsey, Patrick Van Kleef, Sören
Auer, et al. 2015. DBpedia–a large-scale, multilingual knowledge base extracted
from Wikipedia. Semantic Web 6, 2 (2015), 167–195.
[11] Meenakshi Nagarajan, Karthik Gomadam, Amit P Sheth, Ajith Ranabahu,
Raghava Mutharaju, and Ashutosh Jadhav. 2009. Spatio-temporal-thematic analy-
sis of citizen sensor data: Challenges and experiences. In International Conference
Figure 8: Inter-BERT model and BERT Model-to-MHA Jac- on Web Information Systems Engineering. Springer, 539–553.
card Similarity Scores as a measure of closeness of model’s [12] Jianyin Qiu, Bin Shen, Min Zhao, Zhen Wang, Bin Xie, and Yifeng Xu. 2020. A
nationwide survey of psychological distress among Chinese people in the COVID-
prediction to an extensive survey on Mental Health America 19 epidemic: implications and policy recommendations. General psychiatry 33, 2
(MHA). (2020).
[13] Amit Sheth and Pavan Kapanipathi. 2016. Semantic filtering for social data. IEEE
Internet Computing 20, 4 (2016), 74–78.
[14] Chuhan Wu, Fangzhao Wu, Mingxiao An, Jianqiang Huang, Yongfeng Huang, and
and DPR-BERT, subsuming the former and being subsumed by the Xing Xie. 2019. Npa: Neural news recommendation with personalized attention.
In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge
latter in terms of depressive/drug abusive months. Discovery & Data Mining. 2576–2584.
[15] Amir Hossein Yazdavar, Hussein S Al-Olimat, Monireh Ebrahimi, Goonmeet
5 CONCLUSION Bajaj, Tanvi Banerjee, Krishnaprasad Thirunarayan, Jyotishman Pathak, and
Amit Sheth. 2017. Semi-supervised approach to monitoring clinical depressive
In this paper, we model depressiveness, drug abusiveness, and in- symptoms in social media. In Proceedings of the 2017 IEEE/ACM International
Conference on Advances in Social Networks Analysis and Mining 2017. 1191–1198.
formativeness of news articles to assess the dominant category
characterizing each US state during each of the three months (Jan
2020 to Mar 2020). We demonstrate the power of transfer learning
by fine-tuning an attention-based deep learning model on a dif-
ferent domain and use the domain-tuned model for gleaning the
nature of media exposure. Specifically, we use background knowl-
edge bases for measuring depressiveness, drug abusiveness, and
informativeness of news articles. We found out DA-BERT identifies
the most number of state-to-month instances as being exposed
to depressive or drug abuse news content according to the report
5