Leveraging Big Data to Identify Corruption as an SDG Goal 16 Humanitarian Technology

Li, Jiawei; Chen, Wen‐Hao; Xu, Qing; Shah, Neal; Mackey, Timothy

doi:10.1109/ghtc46095.2019.9033129

Cited by 6 publications

(8 citation statements)

References 9 publications

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“…In addition, based on the number of posts collected from each platform, we used a protocol of using either (1) BTM in combination with manual annotation of posts for platforms where there were greater than 10,000 posts or (2) solely relying on manual annotation of posts for platforms with less than 10,000 total posts. The 10,000-post cutoff was deemed appropriate given previous studies that have relied on manual annotation for similarly sized social media data sets and the relative imprecision of BTM compared with manual annotation when examining smaller sized data sets [ 32 ].…”

Section: Methodsmentioning

confidence: 99%

Unsupervised Machine Learning to Detect and Characterize Barriers to Pre-exposure Prophylaxis Therapy: Multiplatform Social Media Study

Xu¹,

Nali²,

McMann³

et al. 2022

JMIR Infodemiology

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Background: Among racial and ethnic minority groups, the risk of HIV infection is an ongoing public health challenge. Pre-exposure prophylaxis (PrEP) is highly effective for preventing HIV when taken as prescribed. However, there is a need to understand the experiences, attitudes, and barriers of PrEP for racial and ethnic minority populations and sexual minority groups.Objective: This infodemiology study aimed to leverage big data and unsupervised machine learning to identify, characterize, and elucidate experiences and attitudes regarding perceived barriers associated with the uptake and adherence to PrEP therapy. This study also specifically examined shared experiences from racial or ethnic populations and sexual minority groups. Methods:The study used data mining approaches to collect posts from popular social media platforms such as Twitter, YouTube, Tumblr, Instagram, and Reddit. Posts were selected by filtering for keywords associated with PrEP, HIV, and approved PrEP therapies. We analyzed data using unsupervised machine learning, followed by manual annotation using a deductive coding approach to characterize PrEP and other HIV prevention-related themes discussed by users. Results:We collected 522,430 posts over a 60-day period, including 408,637 (78.22%) tweets, 13,768 (2.63%) YouTube comments, 8728 (1.67%) Tumblr posts, 88,177 (16.88%) Instagram posts, and 3120 (0.6%) Reddit posts. After applying unsupervised machine learning and content analysis, 785 posts were identified that specifically related to barriers to PrEP, and they were grouped into three major thematic domains: provider level (13/785, 1.7%), patient level (570/785, 72.6%), and community level (166/785, 21.1%). The main barriers identified in these categories included those associated with knowledge (lack of knowledge about PrEP), access issues (lack of insurance coverage, no prescription, and impact of COVID-19 pandemic), and adherence (subjective reasons for why users terminated PrEP or decided not to start PrEP, such as side effects, alternative HIV prevention measures, and social stigma). Among the 785 PrEP posts, we identified 320 (40.8%) posts where users self-identified as racial or ethnic minority or as a sexual minority group with their specific PrEP barriers and concerns.Conclusions: Both objective and subjective reasons were identified as barriers reported by social media users when initiating, accessing, and adhering to PrEP. Though ample evidence supports PrEP as an effective HIV prevention strategy, user-generated posts nevertheless provide insights into what barriers are preventing people from broader adoption of PrEP, including topics that are specific to 2 different groups of sexual minority groups and racial and ethnic minority populations. Results have the potential to inform future health promotion and regulatory science approaches that can reach these HIV and AIDS communities that may benefit from PrEP.

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Section: Methodsmentioning

confidence: 99%

Unsupervised Machine Learning to Detect and Characterize Barriers to Pre-exposure Prophylaxis Therapy: Multiplatform Social Media Study

Xu¹,

Nali²,

McMann³

et al. 2022

JMIR Infodemiology

Self Cite

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“…The works of [14] and [20] present an approach to crime detection based on users' perceptions. The first is based on the post content on the social network Twitter, while the authors of the second created a survey to be applied by public ("detect*" OR "search*" OR "find*" OR "look* for" OR "predict*") AND ("crime" OR "corruption" OR "clue" OR "fraud*" OR "collusion" OR "offence" OR "cartel" OR "malfeasance" OR "prevarication" OR "ghost payroll" OR "ghost employee" OR "bid rigging" OR "irregularity" OR "money laundering"OR "anomaly" OR "suspicious") AND ("public administration" OR "public sector" OR "public procurement" OR "government* procurement" OR "public treasury" OR ("bidding" AND ("public" OR "government*")) OR "public employ*"OR "government* purchas*" OR "government* treasury" OR "public fund") AND ("data mining" OR "data science" OR "text mining" OR "artificial intelligence" OR "data crossing" OR "crossing technologies" OR "data combination" OR "data manipulation" OR "machine learning" OR "neural network" OR "deep learning" OR "cluster analysis" OR "algorithm") AND ("decision support system" OR "dss" OR "knowledge discovery" OR "automated system" OR "automated information system" OR "prototype" OR "online analytical processing" OR "olap" OR "intelligent agent" OR "predictive model*" OR "predictive analytics" OR "model" OR "corruption indicator" OR "approach*")…”

Section: B Work Abstractsmentioning

confidence: 99%

“…Two other works ( [14] and [20]) did not define a specific crime type but were concerned with detecting fraud in a more comprehensive way through opinion collection and user perception. There are also works aimed at finding fraud in the government employee payroll [3] and money laundering [13].…”

Section: Qp1: What Crime Types Against Public Administration Are Most...mentioning

confidence: 99%

Data science to identify crimes against public administration

Souza,

Júnior,

Fontes

et al. 2023

Annals of Computer Science and Information Systems

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Context:The management of public resources is subject to illegal acts and the automatic identification of such acts depends on the analysis of a lot of data. Objective: The object of this work is the analysis of scientific publications through a study based on systematic mapping with the purpose of evaluating them in relation to the use of automated tools to identify crimes against public administration in databases from the perspective of researchers in the data science context. Method: Using PICO strategy (Population, In-tervention, Comparison, and Outcome), a systematic mapping was conducted to find the primary studies in the literature and collect evidence for directing future research. Results: Nineteen works were found that fit the proposed cri-teria. Almost 80% of the studies found seek to identify some type of fraud in bidding processes, obtaining accuracies between 72% and 99%. The research also revealed different techniques for approaching the problem. Considering all the works, the most used databases are bidding bases, lawsuits, public notices and corporate structure of companies, respectively. Conclusions: The work has shown a recent increase in interest in analyzing public data for irregularities. It is expected that this analysis will help control bodies elucidating different ways of detecting crimes against the public administration in an automated way.

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“…A talált NLP-s cikkek között több olyan volt, ami nem annyira tartalmi, mint inkább technikai célra alkalmazta a módszert. Li et al (2019) célja például egy olyan módszer kialakítása, ami felismeri a korrupcióhoz kapcsolódó tweeteket; ezáltal egy közösségi média alapú "surveillance" (felügyelő és jelentő) eszköz kialakítása. Az eszköz célzott beavatkozások (mint hatóságok számára történő jelentések) kidolgozására is felhasználható.…”

Section: A Tanulmányokban Használt Elemzési Módszerekunclassified

Automatizált szöveganalitika a korrupció kutatásában

Katona¹,

Németh²

2021

socio.hu

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Tanulmányunk a természetesnyelv-feldolgozás (Natural Language Processing – NLP) korrupciókutatásban való felhasználását és felhasználhatóságát vizsgálja. Átfogó irodalmi áttekintésünk során a 2000 után született, automatizált szövegelemzésre épülő korrupciókutatások teljeskörű összegyűjtésére és összegzésére törekedtünk az NLP alkalmazás elterjedtségére, illetve lehetőségeire fókuszálva. Lényeges eltéréseket találtunk a felhasznált szöveges adatforrást, a korrupció mérésének módját és az elemzési megközelítést tekintve, ugyanakkor sajnálatosan kevés volt az (adatforrását, módszerét vagy mérési módját tekintve) kevert típusú tanulmány. A klasszikus, a korrupció volumenét vagy a vele kapcsolatos attitűdöt vagy percepciót leíró, illetve észlelésének következményeit vizsgáló munkákon kívül találtunk a korrupció megelőzésére felhasználható eredményeket, sőt intervencióra közvetlenül alkalmasakat is. Az NLP-t csupán néhány tanulmány használta, és ezek egy része sem annyira tartalmi, mint csupán technikai feladatra. Eredményeink szerint az NLP nem nagyon elterjedt még ezen a területen, ugyanakkor az is látható, hogy gyümölcsöző lehet a használata: alternatív eszközként jól támogathatná a tradicionális kvantitatív kutatásokat. Cikkünk célja inspirációt adni az NLP társadalomtudományi felhasználására és felhívni a figyelmet annak beágyazhatóságára a meglevő tudományos diskurzusokba.

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Leveraging Big Data to Identify Corruption as an SDG Goal 16 Humanitarian Technology

Cited by 6 publications

References 9 publications

Unsupervised Machine Learning to Detect and Characterize Barriers to Pre-exposure Prophylaxis Therapy: Multiplatform Social Media Study

Unsupervised Machine Learning to Detect and Characterize Barriers to Pre-exposure Prophylaxis Therapy: Multiplatform Social Media Study

Data science to identify crimes against public administration

Automatizált szöveganalitika a korrupció kutatásában

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