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In a first-of-its-kind effort, social media researchers from Drexel University, Vanderbilt University, Georgia Institute of Technology and Boston University are turning to young social media users to help build a machine learning program that can spot unwanted sexual advances on Instagram. Trained on data from more than 5 million direct messages annotated and contributed by 150 adolescents who had experienced conversations that made them feel sexually uncomfortable or unsafe the technology can quickly and accurately flag risky DMs.

The project, which was recently published by the Association for Computing Machinery in its Proceedings of the ACM on Human-Computer Interaction, is intended to address concerns that an increase of teens using social media, particularly during the pandemic, is contributing to rising trends of child sexual exploitation.

In the year 2020 alone, the National Center for Missing and Exploited Children received more than 21.7 million reports of child sexual exploitation which was a 97% increase over the year prior. This is a very real and terrifying problem, said Afsaneh Razi, PhD, an assistant professor in Drexels College of Computing & Informatics, who was a leader of the research.

Social media companies are rolling out new technology that can flag and remove sexually exploitative images and helps users to more quickly report these illegal posts. But advocates are calling for greater protection for young users that could identify and curtail these risky interactions sooner.

The groups efforts are part of a growing field of research looking at how machine learning and artificial intelligence be integrated into platforms to help keep young people safe on social media, while also ensuring their privacy. Its most recent project stands apart for its collection of a trove of private direct messages from young users, which the team used to train a machine learning-based program that is 89% accurate at detecting sexually unsafe conversations among teens on Instagram.

Most of the research in this area uses public datasets which are not representative of real-word interactions that happen in private, Razi said. Research has shown that machine learning models based on the perspectives of those who experienced the risks, such as cyberbullying, provide higher performance in terms of recall. So, it is important to include the experiences of victims when trying to detect the risks.

Each of the 150 participants who range in age from 13- to 21-years-old had used Instagram for at least three months between the ages of 13 and 17, exchanged direct messages with at least 15 people during that time, and had at least two direct messages that made them or someone else feel uncomfortable or unsafe. They contributed their Instagram data more than 15,000 private conversations through a secure online portal designed by the team. And were then asked to review their messages and label each conversation, as safe or unsafe, according to how it made them feel.

Collecting this dataset was very challenging due to sensitivity of the topic and because the data is being contributed by minors in some cases, Razi said. Because of this, we drastically increased the precautions we took to preserve confidentiality and privacy of the participants and to ensure that the data collection met high legal and ethical standards, including reporting child abuse and the possibility of uploads of potentially illegal artifacts, such as child abuse material.

The participants flagged 326 conversations as unsafe and, in each case, they were asked to identify what type of risk it presented nudity/porn, sexual messages, harassment, hate speech, violence/threat, sale or promotion of illegal activities, or self-injury and the level of risk they felt either high, medium or low.

This level of user-generated assessment provided valuable guidance when it came to preparing the machine learning programs. Razi noted that most social media interaction datasets are collected from publicly available conversations, which are much different than those held in private. And they are typically labeled by people who were not involved with the conversation, so it can be difficult for them to accurately assess the level of risk the participants felt.

With self-reported labels from participants, we not only detect sexual predators but also assessed the survivors perspectives of the sexual risk experience, the authors wrote. This is a significantly different goal than attempting to identify sexual predators. Built upon this real-user dataset and labels, this paper also incorporates human-centered features in developing an automated sexual risk detection system.

Specific combinations of conversation and message features were used as the input of the machine learning models. These included contextual features, like age, gender and relationship of the participants; linguistic features, such as wordcount, the focus of questions, or topics of the conversation; whether it was positive, negative or neutral; how often certain terms were used; and whether or not a set of 98 pre-identified sexual-related words were used.

This allowed the machine learning programs to designate a set of attributes of risky conversations, and thanks to the participants assessments of their own conversations, the program could also rank the relative level of risk.

The team put its model to the test against a large set of public sample conversations created specifically for sexual predation risk-detection research. The best performance came from its Random Forest classifier program, which can rapidly assign features to sample conversations and compare them to known sets that have reached a risk threshold. The classifier accurately identified 92% of unsafe sexual conversations from the set. It was also 84% accurate at flagging individual risky messages.

By incorporating its user-labeled risk assessment training, the models were also able to tease out the most relevant characteristics for identifying an unsafe conversation. Contextual features, such as age, gender and relationship type, as well as linguistic inquiry and wordcount contributed the most to identifying conversations that made young users feel unsafe, they wrote.

This means that a program like this could be used to automatically warn users, in real-time, when a conversation has become problematic, as well as to collect data after the fact. Both of these applications could be tremendously helpful in risk prevention and the prosecution of crimes, but the authors caution that their integration into social media platforms must preserve the trust and privacy of the users.

Social service providers find value in the potential use of AI as an early detection system for risks, because they currently rely heavily on youth self-reports after a formal investigation had occurred, Razi said. But these methods must be implemented in a privacy-preserving matter to not harm the trust and relationship of the teens with adults. Many parental monitoring apps are privacy invasive since they share most of the teen's information with parents, and these machine learning detection systems can help with minimal sharing of information and guidelines to resources when it is needed.

They suggest that if the program is deployed as a real-time intervention, then young users should be provided with a suggestion rather than an alert or automatic report and they should be able to provide feedback to the model and make the final decision.

While the groundbreaking nature of its training data makes this work a valuable contribution to the field of computational risk detection and adolescent online safety research, the team notes that it could be improved by expanding the size of the sample and looking at users of different social media platforms. The training annotations for the machine learning models could also be revised to allow outside experts to rate the risk of each conversation.

The group plans to continue its work and to further refine its risk detection models. It has also created an open-source community to safely share the data with other researchers in the field recognizing how important it could be for the protection of this vulnerable population of social media users.

The core contribution of this work is that our findings are grounded in the voices of youth who experienced online sexual risks and were brave enough to share these experiences with us, they wrote. To the best of our knowledge, this is the first work that analyzes machine learning approaches on private social media conversations of youth to detect unsafe sexual conversations.

This research was supported by the U.S. National Science Foundation and the William T. Grant Foundation.

In addition to Razi, Ashwaq Alsoubai and Pamela J. Wisniewski, from Vanderbilt University; Seunghyun Kim and Munmun De Choudhury, from Georgia Institute of Technology; and Shiza Ali and Gianluca Stringhini, from Boston University, contributed to the research.

Read the full paper here: https://dl.acm.org/doi/10.1145/3579522

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Sliding Out of My DMs: Young Social Media Users Help Train ... - Drexel University

Artificial intelligence (AI) is set to be the most disruptive emerging technology in drug development in 2023, unlocking advanced analytics, enabling automation, and increasing speed across the clinical trial value chain.

Todays clinical trials landscape is being shaped by macro trends that include the Covid-19 pandemic, geopolitical uncertainty, and climate pressures. Meanwhile, advancements in adaptive design, personalisation and novel treatments mean that clinical trials are more complex than ever. Sponsors seek greater agility and faster time to commercialisation while maintaining quality and safety in an evolving global market. Across every stage of clinical research, AI offers optimisation opportunities.

A new whitepaper from digital technology solutions provider Taimei examines the transformative impact of AI on the clinical trials of today and explores how it will shape the future.

The big delay areas are always patient recruitment, site start-up, querying, data review, and data cleaning, explains Scott Clark, chief commercial officer at Taimei.

Patient recruitment is typically the most time-consuming stage of a clinical trial. Sponsors must find and identify a set of subjects, gather information, and use inclusion/exclusion criteria to filter and select participants. And high-quality patient recruitment is vital to a trials success.

Once patients are recruited, they must be managed effectively. Patient retention has a direct impact on the quality of the trials results, so their management is crucial. In todays clinical trials, these patients can be distributed over more than a hundred sites and across multiple geographies, presenting huge data management challenges for sponsors.

AI can be leveraged across patient recruitment and management to boost efficiency, quality, and retention. Algorithms can gather subject information and screen and filter potential participants. They can analyse data sources such as medical records and even social media content to detect subgroups and geographies that may be relevant to the trial. AI can also alert medical staff and patients to clinical trial opportunities.

The result? Faster, more efficient patient recruitment, with the ability to reach more diverse populations and more relevant participants, as well as increase quality and retention. [Using AI], you can develop the correct cohort, explains Clark. Its about accuracy, efficiency, and safety.

Study build can be a laborious and repetitive process. Typically, data managers must read the study protocol and generate as many as 50-60 case report forms (CRFs). Each trial has different CRF requirements. CRF design and database building can take weeks and has a direct impact on the quality and accuracy of the clinical trial.

Enter AI. Automated text reading can parse, categorise, and stratify corpora of words to automatically generate eCRFs and the data capture matrix. In study building, AI is able to read the protocols and pull the best CRF forms for the best outcomes, adds Clark.

It can then use the data points from the CRFs to build the study base, creating the whole database in a matter of minutes rather than weeks. The database is structured for export to the biostatisticians programming. AI can then facilitate the analysis of data and develop all of the required tables, listings and figures (TLFs). It can even come to a conclusion on the outcomes, pending review.

Optical character recognition (OCR) can address structured and unstructured native documents. Using built-in edit checks, AI can reduce the timeframe for study build from ten weeks to just one, freeing up data managers time. We are able to do up to 168% more edit checks than are done currently in the human manual process, says Clark. AI can also automate remote monitoring to identify outliers and suggest the best route of action, to be taken with approval from the project manager.

AI data management is flexible, agile, and robust. Using electronic data capture (EDC) removes the need to manage paper-based documentation. This is essential for modern clinical trials, which can present huge amounts of unstructured data thanks to the rise of advances such as decentralisation, wearables, telemedicine, and self-reporting.

Once the trial is launched, you can use AI to do automatic querying and medical coding, says Clark. When theres a piece of data that doesnt make sense or is not coded, AI can flag it and provide suggestions automatically. The data manager just reviews what its corrected, adds Clark. Thats a big time-saver. By leveraging AI throughout data input, sponsors also cut out the lengthy process of data cleaning at the end of a trial.

Implementing AI means establishing the proof of concept, building a customised knowledge base, and training the model to solve the problem on a large scale. Algorithms must be trained on large amounts of data to remove bias and ensure accuracy. Today, APIs enable best-in-class advances to be integrated into clinical trial applications.

By taking repetitive tasks away from human personnel, AI accelerates the time to market for life-saving drugs and frees up man-hours for more specialist tasks. By analysing past and present trial data, AI can be used to inform future research, with machine learning able to suggest better study design. In the long term, AI has the potential to shift the focus away from trial implementation and towards drug discovery, enabling improved treatments for patients who need them.

To find out more, download the whitepaper below.

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How AI, automation, and machine learning are upgrading clinical trials - Clinical Trials Arena

The M.Sc. program in Machine Learning & Data Science at the Efi Arazi School of Computer Science aims to provide a deep theoretical understanding of machine learning and data-driven methods as well as a strong proficiency in using these methods. As part of this unique program, students with solid exact science backgrounds, but not necessarily computer science backgrounds, are trained to become data scientists. Headed by Prof. Zohar Yakhini and PhD Candidate Ben Galili, this program provides students with the opportunity to become skilled and knowledgeable data scientists by preparing them with fundamental theoretical and mathematical understandings, as well as endowing them with scientific and technical skills necessary to be creative and effective in these fields. The program offers courses in statistics and data analysis, different levels of machine -learning courses as well as unique electives such as a course in recommendation systems and on DNA and sequencing technologies.

M.Sc. student Guy Assa, preparing DNA for sequencing on a nanopore device, in Prof. Noam Shomrons DNA sequencing class, part of the elective curriculum (Credit: private photo)

In recent years, data science methodologies have become a foundational language and a main development tool for science and industry. Machine learning and data-driven methods have developed considerably and now penetrate almost all areas of modern life. The vision of a data-driven world presents many exciting challenges to data experts in diverse fields of application, such as medical science, life science, social science, environmental science, finance, economics, business.

Graduates of the program are successful in becoming data scientists in Israeli hi-tech companies. Lior Zeida Cohen, a graduate of the program says After earning a BA degree in Aerospace Engineering from the Technion and working as an engineer and later leading a control systems development team, I sought out a graduate degree program that would allow me to delve deeply into the fields of Data Science and Machine Learning while also allowing me to continue working full-time. I chose to pursue the ML & Data Science Program, at Reichman University. The program provided in-depth study in both the theoretical and practical aspects of ML and Data Science, including exposure to new research and developments in the field. It also emphasized the importance of learning the fundamental concepts necessary for working in these domains. In the course of completing the program, I began work at Elbit Systems as an algorithms developer in a leading R&D group focusing on AI and Computer Vision. The program has greatly contributed to my success in this position".

As a part of the curriculum, the students execute collaborative research projects with both external and internal collaborators, in Israel and around the world; One active collaboration is with the Leibniz Institute for Tropospheric Research (TROPOS) in Leipzig, Germany. In this collaboration, the students, led by Prof. Zohar Yakhini and Dr. Shay Ben-Elazar, a Principal Data Science and Engineering Manager at Microsoft Israel, as well as Dr. Johannes Bhl from TROPOS, are using data science and machine learning tools in order to infer properties of stratospheric layers by using data from sensory devices. The models developed in the project provide inference from simple devices that achieves an accuracy which is close to that which is obtained through much more expensive measurements. This improvement is enabled through the use of neural network models (deep learning).

Results from the TROPOS project: a significant improvement in the inference accuracy. Left panel: actual atmospheric status as obtained from the more expensive measurements (Lidar + Radar) Middle panel: predicted status as inferred from Lidar measurements using physical models. Right panel: status determined by the deep learning model developed in the project.

Additional collaborations include a number of projects with Israeli hospitals such as Sheba Tel Hashomer, Beilinson Hospital, and Kaplan Medical Center, as well as with the Israel Nature and Parks Authority and with several hi-tech companies.

PhD candidate Ben Galili, Academic Director of Machine Learning and Data Science Program (Credit: private photo)

Several research and thesis projects are led by students in the program addressing data analysis questions related to spatial biology the study of molecular biology processes in their bigger location context. One project, led by student Guy Attia and supervised by Dr. Leon Anavy addressed imputation methods for spatial transcriptomics data. A second one, led by student Efi Herbst, aims to expand the inference scope of data from spatial transcriptomics, into molecular properties that are not directly measured by the technology device.

According to Maya Kerem, a recent graduate, the MA program taught me a number of skills that would enable me to easily integrate into a new company based on the knowledge I gained. I believe that this program is particularly unique because it always makes sure that the learnings are applied to industry-related problems at the end of each module. This is a hands-on program at Reichman University, which is what drew me to enroll in this MA program.

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This article was written in cooperation with Reichman University

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An M.Sc. computer science program in RUNI, focusing on machine learning - The Jerusalem Post