Leveraging Twitter and Neo4j to Study the Public Use of Opioids in the USA

Soni, Disha; Ghanem, Thanaa M.; Gomaa, Basma; Schommer, Jon C.

doi:10.1145/3327964.3328501

Cited by 5 publications

(4 citation statements)

References 7 publications

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“…In order to preserve temporal information, a data model for time-varying social media data (Cattuto et al 2013;Durand et al 2017) is proposed using Neo4j (NoSQL graph database). Cypher query language (CQL) is used to analyze a huge volume of twitter dataset (Soni et al 2019;Boselli et al 2017;Zhang et al 2017). These research work explores the suitability of Neo4j graph database for both efficient storage, and fast query processing of social media data (Boselli et al 2017;Zhang et al 2017;Angles and Gutierrez 2008;Fernandes and Bernardino 2018;Sharma 2015;Allen et al 2019).…”

Section: Related Workmentioning

confidence: 99%

“…In this way, social media has been playing a major role in information sharing at a large scale due to its easy access, low cost, and fast dissemination of information. However, social media has a positive impact in expanding society's access to information as well as it quickly amplifies the influence of digital content (Soni et al 2019). However, no one cares about the truthiness, quality, and credibility of that content (Soto et al 2018;Yang et al 2019).…”

Section: Introductionmentioning

confidence: 99%

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Social data provenance framework based on zero-information loss graph database

Rani

Goyal

Gadia

2022

Soc. Netw. Anal. Min.

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Social media has become a common platform for global communication across the world due to its rapid dissemination of information among a large audience. Its popularity has raised a crucial challenge to capture the social data provenance of a piece of information published on social media. Social data provenance describes the source and deriving process of a digital content, and when it is updated since its existence? It aids in determining reliability, authenticity, and trustworthiness of a piece of information and explaining how, when, and by whom this information is published. In this paper, we propose a social data provenance (SDP) framework based on zero-information loss graph database (ZILGDB). The proposed framework supports historical data queries, and querying through time using updates management in ZILGDB. It has the capability to capture provenance for a query set including select, aggregate, and data update queries with insert, delete, and update operations. It also provides a detailed provenance analysis through visualization and with efficient multi-depth provenance querying support, to determine both direct and indirect sources of a digital content. We conduct a real-life use case study to evaluate the usefulness of proposed framework in terrorist attack investigation. We evaluate the performance of proposed framework in terms of average execution time for various provenance queries, and provenance capturing overhead for a query set.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Social data provenance framework based on zero-information loss graph database

Rani

Goyal

Gadia

2022

Soc. Netw. Anal. Min.

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“…Most of the other works concentrated on analyzing drug use/abuse trends [4][5][6][7][8][9][10][11]. Others conducted research on monitoring social media such as Youtube [12], Twitter [13][14][15], and Instagram [16]. Although those efforts have positively contributed toward critical aspects of combating real-time drug use/abuse issues, they exhibited some crucial challenges.…”

Section: Introductionmentioning

confidence: 99%

The Efficacy of PRISTINE: Revealing Concealed Opioid Crisis Trends via Reddit Examination

Alhamadani

Sarkar

Behal

et al. 2023

Preprint

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This work is an extension of our previous efforts to combat the drug abuse epidemic which has been on the rise in the past few years []. We expand our developed framework PRISTINE ((opioid crisis detection on reddit)) to investigate the effectiveness of the framework on detecting opioids crisis trends on an expanded dataset from the two subreddits r/dugs and r/opiates. In this endeavor, we demonstrate the effectiveness of utilizing the DQE algorithm in identifying drug-related and evolving drug terms. we conduct comprehensive case studies for the seven drug categories and showcase the most associated keywords for each drug class and their slang/street names. In addition, we provide a case study on one of the most significant opioid crisis contributors to drug overdose deaths in the United States. Our case studies revealed hard-to-find drugrelated terms which we hope to contribute to mitigating this crisis. We additionally include a new analysis to investigate the efficacy of applying PRISTINE in categorizing subreddits into fine-grained drug classes. The new analysis includes a case study that classifies anonymized and lengthy subreddit comments into their correct drug class. The analysis shows the strong performance of PRISTINE and demonstrates that the framework can be applied to a wide range of subreddit comments. We finally include the performed extensive experiments to show the effectiveness of the overall performance of the proposed framework.

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“…At the same time, NoSQL databases have been gaining traction. In particular, the space of graph databases [4,50,91,92] is growing at a rapid pace, as they have been shown to be a great fit for tasks such as fraud detection, drug discovery [100], recommendation engines, and data visualization [24,58]. Graph databases store data as property graphs [3,44,64], which emphasize relationships between data.…”

mentioning

confidence: 99%

Object Graph Programming

Thimmaiah,

Lampropoulos,

Rossbach

et al. 2024

Proceedings of the IEEE/ACM 46th International Conference on Software Engineering

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We introduce Object Graph Programming (OGO), which enables reading and modifying an object graph (i.e., the entire state of the object heap) via declarative queries. OGO models the objects and their relations in the heap as an object graph thereby treating the heap as a graph database: each node in the graph is an object (e.g., an instance of a class or an instance of a metadata class) and each edge is a relation between objects (e.g., a field of one object references another object). We leverage Cypher, the most popular query language for graph databases, as OGO's query language. Unlike LINQ, which uses collections (e.g., List) as a source of data, OGO views the entire object graph as a single "collection". OGO is ideal for querying collections (just like LINQ), introspecting the runtime system state (e.g., finding all instances of a given class or accessing fields via reflection), and writing assertions that have access to the entire program state. We prototyped OGO for Java in two ways: (a) by translating an object graph into a Neo4j database on which we run Cypher queries, and (b) by implementing our own in-memory graph query engine that directly queries the object heap. We used OGO to rewrite hundreds of statements in large open-source projects into OGO queries. We report our experience and performance of our prototypes.

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Leveraging Twitter and Neo4j to Study the Public Use of Opioids in the USA

Cited by 5 publications

References 7 publications

Social data provenance framework based on zero-information loss graph database

Social data provenance framework based on zero-information loss graph database

The Efficacy of PRISTINE: Revealing Concealed Opioid Crisis Trends via Reddit Examination

Object Graph Programming

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