=Paper=
{{Paper
|id=Vol-2327/ESIDA6
|storemode=property
|title=Exploiting Literature-Based Discovery to Study Effects of Bullying
|pdfUrl=https://ceur-ws.org/Vol-2327/IUI19WS-ESIDA-6.pdf
|volume=Vol-2327
|authors=S.M.Shamimul Hasan
|dblpUrl=https://dblp.org/rec/conf/iui/Hasan19
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==Exploiting Literature-Based Discovery to Study Effects of Bullying==
https://ceur-ws.org/Vol-2327/IUI19WS-ESIDA-6.pdf
Exploiting Literature-Based Discovery to Study
Effects of Bullying
S.M.Shamimul Hasan
Computational Sciences and Engineering Division
Oak Ridge National Laboratory
Oak Ridge, TN, USA
hasans@ornl.gov
ABSTRACT ACM Reference Format:
Bullying represents an aggressive behavioral problem. It can S.M.Shamimul Hasan. 2019. Exploiting Literature-Based Discovery
to Study Effects of Bullying. In Joint Proceedings of the ACM IUI
create severe mental illness in children, adolescents, and
2019 Workshops, Los Angeles, USA, March 20, 2019 , 8 pages.
adults. Researchers are interested in studying bullying to
understand its long-term negative consequences and design
better-targeted intervention programs. Broadly researchers 1 INTRODUCTION
in psychology and education have conducted studies on bul- Bullying is an abuse delivered in the form of threatening,
lying without using advanced analytical features offered by demeaning, and belittling a person by a physically stronger
the computer science domain. However, researchers could individual or one with a higher social status than the vic-
benefit from power of advanced analytics for an in-depth tim [18]. Children, adolescents, and even adults are victims
understanding of bullying. In this paper, we present graph- of bullying [21]. However, young people are at the high-
based advanced analytics by employing a literature-based est risk [1]. Bullying can create serious mental distress [10].
discovery (LBD) approach to study the direct and indirect Moreover, combined with additional risk factors, being in-
consequences of bullying from published literature. In addi- volved in bullying, increases the possibility of engaging in
tion, we describe a graph-based study on the evolution of re- suicide-related behaviors [10]. Furthermore, not only the
search on bullying consequences in the last two decades. Our victims but the atmosphere is affected by bullying. Research
graph-based study reveals a summary view of associations studies show that individuals who experienced bullying be-
of bullying with other diseases and disorders from published havior problems at school age have a tendency to participate
literature. Moreover, we employ literature reference infor- in criminal behavior as adults, including racial attacks, dam-
mation to quantify the strength of the graph-derived rela- age to public property, sexual attacks and even killing [8].
tionships. Furthermore, we propose an interactive Graphical Bullying is an increasingly interesting topic for researchers,
User Interface (GUI) design to study bullying. We demon- because of growing evidence demonstrating long-term nega-
strate that LBD is a valuable approach to perform advanced tive outcomes for both bullies and victims. The researchers
analytics on bullying that helps us to understand the severity aim to identify the causes of bullying in order to find effec-
of its consequences. tive intervention programs targeting the risk and protective
factors related to the involvement in bullying. However, it is
CCS CONCEPTS difficult to study bullying behavior because it is frequently
• Software and its engineering → Software creation covered up or fabricated (eheck [22] for detail) [36]. Histori-
and management. cally, experts in psychology and education have conducted
studies on bullying but other fields such as computer science,
big data, and virtual communities can provide a significant
KEYWORDS
contribution by offering new perspectives and innovative
bullying, literature-based discovery, graph analytics approaches to studying bullying and developing efficient
bullying interventions [15].
Despite the fact that the bully-victim problem had existed
previously, the first systematic studies were only conducted
in the early 1970s [26, 34]. In the early stage, studies were
IUI Workshops’19, March 20, 2019, Los Angeles, USA
performed in Scandinavia, US, Japan, Australia, and UK in
Copyright © 2019 for the individual papers by the papers’ authors. Copying
permitted for private and academic purposes. This volume is published and
the, 1970s, 1980s and 1990s [27]. As stated in US national
copyrighted by its editors. data, approximately 15% of youngsters experienced cyber-
bullying and 20% were bullied at school in 2013. Moreover,
IUI Workshops’19, March 20, 2019, Los Angeles, USA Trovato and Tobin, et al.
8% of students reported being bullied daily [15]. The rate of Semantic MEDLINE
bullying among children is fairly consistent; nevertheless, Semantic MEDLINE is a web-based application that pro-
researchers have found bullying among adults as well [29]. vides biomedical document retrieval, summarization, and
This phenomenon is particularly prominent in the work en- visualization services [30]. This application uses MEDLINE
vironment. According to the “Workplace Bullying Survey citations generated from PubMed searches [19, 30]. Semantic
2014,” 72% of Americans reported being aware of bullying in MEDLINE employs a natural language processing tool called
the workplace, 21% witnessed it, and 27% suffered abusive SemRep to find semantic relationships (or predications) from
conduct at work [37]. the titles and abstracts of PubMed citations [38]. SemRep
In this paper, we present a summarized view of bully- examines each sentence in PubMed titles and abstracts to
ing’s direct and indirect consequences. The literature-based extract meaningful relationships [30]. For example, SemRep
discovery (LBD) method is employed to identify bullying’s obtains the relation psychological distress COEXISTS_WITH
short-term and long-term impacts from the published litera- bullying from the following citation “The Impact of School
ture. Moreover, we present a systematic discussion on how Social Support and Bullying Victimization on Psychologi-
research on bullying consequences evolves over time. We cal Distress among California Adolescents (PMID:27708555).
utilize Semantic MEDLINE, a web application that manages However, all the SemRep relations are not meaningful. For
the outcomes of PubMed searches by visualizing SemRep example, Semantic MEDLINE shows that citation [14] cre-
predications collected from MEDLINE citations [24]. We ates bullying and vitelliform dystrophy relationship. However,
demonstrate that LBD is a useful approach for domain re- there exists no such relationship in citation [14]. Thus, it is
searchers, to discover logical relationships between bullying necessary for us to read the citations carefully to find out
and its harmful impacts. the soundness and strength of the relationships.
We review previous work on LBD in Section 2. In Sec- SemRep relationships are presented in a triple form as it
tion 3, we present our research methodology. In Section 4, contains three parts: subject, predicate, and object. In the
we provide a detail discussion of our research results. In Sec- above example, the subject is psychological distress, the pred-
tion 5, we discuss propose user interface design and Section 6 icate is COEXISTS_WITH, and the object is bullying. Sub-
concludes the paper. jects and objects represent concepts taken from the UMLS’s
Metathesaurus, and predicates are collected from the Se-
mantic Network (SN) [24]. Semantic MEDLINE provides vi-
2 BACKGROUND
sualization of the relationships as a graph (or network) by
Literature-based discovery (LBD) uses published literature considering the subject and object as nodes, and the predicate
to identify new interesting relationships between existing as an edge. The graph edge provides citation information
knowledge regarding a given phenomenon [33]. One of the and the sentence from which the relationship is extracted.
early work on LBD was conducted by Don R. Swanson. The We present an example visualization in Fig. 1 where psycho-
author employed LBD technique to study fish oil [33]. Swan- logical distress and bullying maintain a COEXISTS_WITH
son apply LBD to find the connection between Migraine and relationship (left side of the figure). The citation sentence
Magnesium as well [12]. Hristovski, Friedman, Rindflesch, from which the relationship is extracted is shown in red on
and Peterlin proposed to use semantic predication to increase the right side of the figure. Semantic MEDLINE provides nu-
the strength of the LBD [17]. Miller et al. employed LBD to do merous node types and relations (edges) based on a citation
hypothesis testing. In their study, authors found that testos- search. We refer readers to the SemMed documentation for
terone and sleep have a connection via cortisol [24]. Gordon additional discussion on this topic [4].
and Lindsay utilized LBD to find the link between Raynaud’s
disease and dietary fish oil [13].
Find Effects of Bullying
We utilize semantic MEDLINE to discover direct and indirect
3 METHODOLOGY effects of bullying. We use the free UMLS license to access
There are two main components of our research. First, we the semantic MEDLINE.
study the direct and indirect effects of bullying. Second, we To find the direct effects of bullying, we query seman-
examine the evolution of the patterns depicted in bullying tic MEDLINE with the keyword “bullying” and pursue the
consequences in published literature. We employ semantic following steps:
MEDLINE tool to conduct our research. We present descrip-
tion of the semantic MEDLINE and our research components Step 1: We use the date range (01/01/1900 - 06/30/2017) and
below. Throughout the paper, Unified Medical Language the search options (Most recent: 500 citations) to
System (UMLS) concept names are italicized. return the relevant citations.
Exploiting Literature-Based Discovery to Study Effects of Bullying IUI Workshops’19, March 20, 2019, Los Angeles, USA
Figure 1: Semantic MEDLINE web application.
Step 2: We summarize the citation by using Summary Type: citation title(s) to collect “Cited By” (another citation refers
Treatment and Disease and UMLS concept that occur to a relationship citation) information.
in the relations the maximum number of times. We Note: “Cited By” information is one of importance mea-
also check the “More Relations” checkbox to utilize surement metric. However, it may not be appropriate for
all the relations. Summarization provides us rela- some cases.
tions in a subject, predicate, and object format. It
also presents the citation sentence (from where the 4 DISCUSSION
relation is extracted) and PMID.
Effects of Bullying
Step 3: We visualize the summarized results in a graph where
nodes (UMLS concepts) are the subjects and objects, In the following section, we present our discussion on di-
while edges are predicates (from the Semantic Net- rect and indirect effects of bullying based on literature-based
work). We use the COEXISTS_WITH relation in our discovery. We query semantic MEDLINE with keyword “bul-
study. lying” and use the date range from 01/01/1900 to 06/30/2017,
Step 4: We obtain the corresponding citations for each rela- which provides the recent citations on bullying. We obtain
tion from the graph edge. Finally, we read the citation many relationships from semantic MEDLINE. Furthermore,
to identify meaningful relations. we discuss few relations as examples.
To find indirect effects of bullying, we first search semantic Direct Effect. We observe that bullying concept appears di-
MEDLINE with all the UMLS concepts that co-occur with rectly with the following concepts: 1) suicide, 2) alcohol and
“bullying” (example: Fig. 2-Level 2) and follow the four steps other drugs (AOD) use, 3) metabolic syndrome, 4) psychological
mentioned earlier. Next, with the results obtained in the first distress, 5) inflammation, 6) family conflict, and 7) victimizing
phase, we search semantic MEDLINE again (see example: others. The relationship between bullying and suicide comes
Fig. 2-Level 3) and repeat the above four steps. from the citation [32] where authors study “why bullying
leads to suicidal thoughts and behaviors.” The study per-
Exploring the Evolution of Research on Bullying formed on 340 depressed adolescents and their ages were
We aim to understand the evolution of bullying research 13-19 years. In this study, it was observed that controlling
over time. We consider COEXISTS_WITH relationship. We for internalizing symptoms, age, overt and reputational bul-
observe from semantic MEDLINE that first UMLS concept lying maintains an association with periodic past month
COEXISTS_WITH bullying in the year 1995. Next, from 1995, suicide attempts. Moreover, the authors noted that for male
we expand our search date by five-year periods leading to victims, victimization leads to risky behavior engagement
bullying COEXISTS_WITH graph for the years 1995, 2000, before suicide attempts whereas for female victims, bullying
2005, 2010, 2015, and 2017 (Semantic MEDLINE provides was directly related to suicide attempts [32]. Bullying and
data up to the year of 2017). We generate six graphs with AOD use relation comes from citation [16]. In their study [16],
numerous relations. To quantify the importance of a rela- the authors found that in spite of the severity of internalizing
tionship, we search Google Scholar [2] with relationship’s problems, substance use maintains independent connections
IUI Workshops’19, March 20, 2019, Los Angeles, USA Trovato and Tobin, et al.
Figure 2: In this figure, we present our three-level approach example for bullying’s indirect effects identification. In Level 1,
we present all the concepts co-occur (first neighbor) with bullying. One of them is psychological distress. In Level 2, we show
psychological distress’s first neighbors and Epilepsy is one of them. In Level 3, we display Epilepsy’s first neighbors.
with both negative peer norms and bullying. The citation [23] distress has significant impact on epilepsy. We search seman-
provides bullying’s co-occurrence with metabolic syndrome tic MEDLINE with epilepsy and find that cognitive deficit has
and inflammation. The citation [23] describes that childhood a relationship with epilepsy [31].
bullying has no relationship with metabolic syndrome and We discussed the connection between the bullying and sui-
inflammation. Hence, it means that not all the relationships cide before. Now, we search semantic MEDLINE with concept
present in semantic MEDLINE are positively correlated. The suicide and find that suicide has relationship with eating dis-
relationship between bullying is directly connected to psycho- orders [7]. In the citation [7], via self-image-based prediction,
logical distress, and bullied young people are susceptible to the authors showed that suicide has association with eat-
experience serious psychological distress (SPD) twice more ing disorders. Subsequently, we search semantic MEDLINE
than non-bullied young people [39]. Bullying is also con- with eating disorders and find that it has a relationship with
nected to family conflict via psychological distress [35]. Many obesity [25]. In the citation [25], the authors demonstrate a
citations (e.g., [28], [9]) in semantic MEDLINE suggest that connection between eating disorders and obesity by distin-
bullying concept maintains ISA (sameAS) relationship with guishing “Fear of Obesity” and “Self-Esteem Based on Shape
victimizing others. Hence, in our study, we consider them and Weight.” However, their study has some limitations.
synonymous.
Indirect Effect. In the earlier section (Direct Effect), we men-
tion that bullying has a relation with psychological distress [39].
We search semantic MEDLINE with psychological distress and Evolution of Bullying Research Literature
find that it has a relationship with at risk for suicide, which Our study shows that bullying research is continuously grow-
is shown in citation [6]. In citation [6], the presented results ing as indicated in Fig. 3, which shows relationship of bully-
showed that Suicidal behavior is positively associated to psy- ing with other concepts that have been established within
chological distress. Next, we search semantic MEDLINE with various time frames. According to Fig. 3(a), aggressive behav-
at risk for suicide. We find that at risk for suicide maintains ior is the first disorder that COEXISTS_WITH with bullying
relationship with diabetes mellitus suggested by citation [11]. in the year 1995. The 1995 graph contains only two nodes
In the citation [11], the authors observe that diabetes mellitus and one edge. After five years, in the year 2000, in addition
is strongly connected with an increase in suicidal behaviors to aggressive behavior, bullying is co-occurring with psychi-
and ideation. atric problem, victimizing other and social fear (Fig. 3(b)).
While bullying has relation with psychological distress [39], The 2000 citation graph contains five nodes and three edges.
psychological distress has a relationship with epilepsy [20]. That is, the research work in the time-frame 1995-2000 has
In the citation [20], the authors discussed that psychological established these additional relationships.
Exploiting Literature-Based Discovery to Study Effects of Bullying IUI Workshops’19, March 20, 2019, Los Angeles, USA
(a) Bullying research graph from 01/01/1900 to 06/30/1995. (b) Bullying research graph from 01/01/1900 to 06/30/2000.
(c) Bullying research graph from 01/01/1900 to 06/30/2005. (d) Bullying research graph from 01/01/1900 to 06/30/2010.
(e) Bullying research graph from 01/01/1900 to 06/30/2015. (f) Bullying research graph from 01/01/1900 to 06/30/2017.
Figure 3: In this figure we show, evolution of bullying research literature graph. Here we present all the COEXISTS_WITH
relationships.
IUI Workshops’19, March 20, 2019, Los Angeles, USA Trovato and Tobin, et al.
Considering bullying and victimizing other synonymous, bullying? An exploration of online harassment by known
as shown in Fig. 3(c), in the year 2005, the number of edges (re- peers and online-only contacts” is cited 533 times. Similarly,
lationships) doubled from 2000. Moreover, Fig. 3(c) illustrates for Fig. 3(e) the connection between victimizing others and
some of the serious consequences of bullying like violence, psychiatric problem is important because corresponding cita-
learning disability, and depressive disorder. The research effort tion “Cross-national time trends in bullying victimization in
on bullying increases significantly in the year 2010 as the cor- 33 countries among children aged 11, 13 and 15 from 2002 to
responding graph contains 15 nodes and 17 edges (Fig. 3(d)). 2010” is cited 73 times. Finally, for the year 2017 (Fig. 3(f)), we
Dangerous relations like completed suicide, at risk for suicide, acknowledge Victimizing others and Psychosocial problem is
and retinoblastoma are occurred in this graph. Also, Fig 3(d) a vital relationship (citation title: “Consequences of bullying
presents some interesting secondary relationship of bullying. victimization in childhood and adolescence: A systematic
For example, bullying occurs with aggressive behavior and review and meta-analysis”, cited by 20 times). We observe
aggressive behavior occurs with obesity and alcohol and other that many mental diseases and disorders are co-occurring
drugs (AOD) use. In the graph for 2005, depressive disorder with bullying over the time leading to strong ties between
maintains an indirect relationship with bullying (depressive bullying and mental disorders.
disorder–victimizing others–violence–bullying). However, in
the 2010 bullying graph depressive disorder maintains a di- 5 USER INTERFACE
rect relationship with bullying. In the year 2015, the graph A Graphical User interface (GUI) can be developed on top
expands by a large margin, which contains 21 nodes and of semantic MEDLINE and Google Scholar citation data for
24 edges. This graph shows a direct relation between bul- interactive data analysis. Semantic MEDLINE data is avail-
lying and suicide. We consider it an important relationship able in [5]. “Scholarly” software [3] can be employed to get
because numerous research studies are performed on this Google Scholar data. Next, it is possible to create a Resource
relationship. A number of interesting concepts appear in the Description Framework (RDF) database based on semantic
graph (Fig. 3(e)); in the following we provide some examples: MEDLINE and Google Scholar datasets and a GUI on top
autism spectrum disorder, paranoia, drug abuse, narcissism, of the RDF database. RDF database allows incorporation of
etc. Besides, like earlier, in the 2010 bullying graph psycholog- ontologies as well.
ical distress maintains an indirect relationship with bullying We propose a web-based system called “Bullying Ana-
(psychological distress–victimizing other–bullying). However, lytics” which support semantic search, indirect effect find-
psychological distress maintains a direct relationship with ing, and aggregated statistics. We present system designs in
bullying in 2015 graph. In 2017 graph (Fig. 3(f)), we have 21 Figs. 4, 5, 6.
nodes and 21 edges. In this case, the growth is smaller as
the gap between the year 2015 and 2017 is only two years.
Semantic Search
However, in these two recent years, interesting concepts
such as family conflict, self-injurious behavior, chronic disease, Semantic Search permits users to formulate user define queries
paranoid ideation, and inflammation appear in the graph. with a GUI that uses ontology-driven search. Fig. 4 shows
From Semantic MEDLINE, we collect citation titles of how Semantic Search retrieves bullying literature that con-
the relations available (shown in Fig. 3). We search Google tains “Female” and “Depression” related terms and the to-
Scholar with these titles to find their “cited-by" count. The tal cited-by count is more than or equal 10. By using “Add
cited by information is collected on April 3, 2018. The citation Statement” button, researchers can incrementally formulate
with title “Bullying: its roots in pre-school aggression.” repre- a complex query which helps to find precise information.
sents the single edge in the graph shown in Fig. 3(a). We did Query results (literature title, published year, and cited-by)
not find this citation in Google Scholar. Fig. 3(b)’s top citation are presented in the results section.
title is “Bullying behaviour and psychosocial health among
school students in New South Wales, Australia: cross sec- Pathfinder
tional survey”, that established the relation between bullying This service allows users to find indirect effects of bullying
and psychiatric problem, has been cited 581 times. For 2005 and interactive graph-based visualization from published
graph (Fig. 3(c)), we identify relationship between victim- literature. A user can define hope size as well. Fig. 5 presents
izing others and depressive disorder very important because how bullying indirect effects can be retrieved where hope size
the corresponding citation (title: Does bullying cause emo- is 3 and relation types are COEXISTS_WITH and CAUSES.
tional problems? A prospective study of young teenagers) Users can click an edge of the graph to get detail publication
is cited 1,057 times. We consider the connection between information. Moreover, users can check whether a edges in
bullying and harassment are important for (Fig. 3(d)) because the path is correct or not and provide “Correct” or “Incorrect”
corresponding citation “Does online harassment constitute information through “Feedback” section. Path graph updates
Exploiting Literature-Based Discovery to Study Effects of Bullying IUI Workshops’19, March 20, 2019, Los Angeles, USA
Figure 4: Semantic Search. Figure 6: Aggregated Statistics.
based on users feedback. Pathfinder service allows users to 6 CONCLUSION AND FUTURE WORK
find a long chain of indirect effect interactively. In this paper, we describe an LBD approach to study bullying.
We show that the LBD approach is capable of performing
advanced analytics on bullying. In this paper, we present a
small number of direct and indirect consequences of bullying.
Our future work include development of a software tool to
find the long chain of meaningful indirect consequences
automatically. It is challenging to design a software that can
understand literature semantics. We also plan to use graph
algorithms (e.g., PageRank) for advanced analytics.
7 ACKNOWLEDGMENTS
This manuscript has been authored by UT-Battelle, LLC un-
der Contract No. DE-AC05-00OR22725 with the U.S. Depart-
ment of Energy. The United States Government retains and
the publisher, by accepting the article for publication, ac-
knowledges that the United States Government retains a non-
exclusive, paid-up, irrevocable, world-wide license to publish
or reproduce the published form of this manuscript, or allow
others to do so, for United States Government purposes. The
Figure 5: Pathfinder.
Department of Energy will provide public access to these re-
sults of federally sponsored research in accordance with the
DOE Public Access Plan (http://energy.gov/downloads/doe-
Aggregated Statistics public-access-plan).
We especially thank Dr. Maleq Khan for his useful guide-
Fig. 6 shows aggregation service that “Bullying Analytics”
lines and suggestions that greatly improved the paper.
provides. Users can select a start and end date. Based on
users selection the GUI presents the total number of bullying- REFERENCES
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