Characterizing Awareness of Schizophrenia Among Facebook Users by Leveraging Facebook Advertisement Estimates

Social networks use in the context of Schizophrenia: a review of the literature

Schizophrenia is a persistent mental health condition that, while presenting challenges, underscores the dynamic nature of cognitive functions and encourages a unique perspective on how individuals engage with their surroundings. Social networks, as a means of communication of great importance at the present time, are for this type of people a way of interacting with their environment with a high level of security. The aim is to find out how schizophrenia is dealt with in different social networks and to differentiate between different types of articles dealing with the use of Facebook, X (former Twitter), YouTube, TikTok, Instagram, and Weibo. A total of 45 articles to i) Social networks used, ii) Country of analyzed users, iii) age of the users analyzed, iv) focus of the analyzed manuscript (mental health literacy, stigmatization, detection of patterns associated with schizophrenia, and Harmful substance use). It was observed that 45.45% of the studies analyzed were conducted in the USA population, followed by UK and China (13.64%). The most analyzed social networks were those based on audiovisual communication (60%). Furthermore, the two main foci addressed in these articles were: stigmatization of schizophrenia with 16 articles (35.55%), following by the prediction of schizophrenia-detecting patterns with 15 articles (33.33%) and the use of social networks to stigmatize people with schizophrenia (38%) and only 14 articles (31.11%) were focused on mental health literacy. Likewise, it was found that there is great potential in the use of the analysis of the content generated, as possible predictors of the presence of this disease, which would allow rapid detection and intervention for psychosis and schizophrenia.

Social media and schizophrenia: An update on clinical applications

Social media has redesigned the landscape of human interaction, and data obtained through these platforms are promising for schizophrenia diagnosis and management. Recent research shows mounting evidence that machine learning analysis of social media content is capable of not only differentiating schizophrenia patients from healthy controls, but also predicting conversion to psychosis and symptom exacerbations. Novel platforms such as Horyzons show promise for improving social functioning and providing timely access to therapeutic resources. Social media is also a considerable means to assess and lessen the stigma surrounding schizophrenia. Herein, the relevant literature pertaining to social media and its clinical applications in schizophrenia over the past five years are summarized, followed by a discussion centered on user feedback to highlight future directions. Social media provides valuable contributions to a multifaceted digital phenotype that may improve schizophrenia care in the near future.

Using social media advertisement data to monitor the gender gap in STEM: opportunities and challenges

Boosting the number of women and girls entering careers involving STEM (Science, Technology, Engineering and Maths) is crucial to achieving gender equality, one of the UN Sustainable Development Goals. Girls and women tend to gravitate away from STEM fields at multiple stages from childhood through mid-career. The leaky pipeline is a metaphor often used to describe the loss of women in STEM and arguably other fields before reaching senior roles. Do interests expressed on social media mirror the leaky pipeline phenomenon? In this article, we collected advertisement data (reach estimates) from Facebook and Instagram disaggregated by US metros, age, gender, and interests related to STEM. We computed the Gender Gap Index (GGI) for each US metro and age group. We found that on Instagram, the GGIs for interest in Science decrease as users' age increases, suggesting that relatively there is evidence that that women, compared to men, are losing interest in STEM at older ages. In particular, we find that on Instagram, there are plausible relative trends but implausible absolute levels. Nevertheless, is this enough to conclude that online data available from Instagram mirror the leaky pipeline phenomenon? To scrutinize this, we compared the GGIs for an interest in Science with the GGIs for placebo interests unrelated to STEM. We found that the GGIs for placebo interests follow similar age patterns as the GGIs for the interest in Science across US metros. Second, we attempted to control for the time spent on the platform by computing a usage intensity gender ratio based on the difference between daily and monthly active users. This analysis showed that the usage intensity gender ratio is higher among teenagers (13-17 years) than other older age groups, suggesting that teenage girls are more engaged on the platform that teenage boys. We hypothesize that usage intensity differences, rather than inherent interest changes, might create the illusion of a leaky pipeline. Despite the previously demonstrated value and huge potential of social media advertisement data to study social phenomena, we conclude that there is little evidence that this novel data source can measure the decline in interest in STEM for young women in the USA.

Psychosocial Effects of the COVID-19 Pandemic: Large-scale Quasi-Experimental Study on Social Media

BACKGROUND

The COVID-19 pandemic has caused several disruptions in personal and collective lives worldwide. The uncertainties surrounding the pandemic have also led to multifaceted mental health concerns, which can be exacerbated with precautionary measures such as social distancing and self-quarantining, as well as societal impacts such as economic downturn and job loss. Despite noting this as a "mental health tsunami", the psychological effects of the COVID-19 crisis remain unexplored at scale. Consequently, public health stakeholders are currently limited in identifying ways to provide timely and tailored support during these circumstances.

OBJECTIVE

Our study aims to provide insights regarding people's psychosocial concerns during the COVID-19 pandemic by leveraging social media data. We aim to study the temporal and linguistic changes in symptomatic mental health and support expressions in the pandemic context.

METHODS

We obtained about 60 million Twitter streaming posts originating from the United States from March 24 to May 24, 2020, and compared these with about 40 million posts from a comparable period in 2019 to attribute the effect of COVID-19 on people's social media self-disclosure. Using these data sets, we studied people's self-disclosure on social media in terms of symptomatic mental health concerns and expressions of support. We employed transfer learning classifiers that identified the social media language indicative of mental health outcomes (anxiety, depression, stress, and suicidal ideation) and support (emotional and informational support). We then examined the changes in psychosocial expressions over time and language, comparing the 2020 and 2019 data sets.

RESULTS

We found that all of the examined psychosocial expressions have significantly increased during the COVID-19 crisis-mental health symptomatic expressions have increased by about 14%, and support expressions have increased by about 5%, both thematically related to COVID-19. We also observed a steady decline and eventual plateauing in these expressions during the COVID-19 pandemic, which may have been due to habituation or due to supportive policy measures enacted during this period. Our language analyses highlighted that people express concerns that are specific to and contextually related to the COVID-19 crisis.

CONCLUSIONS

We studied the psychosocial effects of the COVID-19 crisis by using social media data from 2020, finding that people's mental health symptomatic and support expressions significantly increased during the COVID-19 period as compared to similar data from 2019. However, this effect gradually lessened over time, suggesting that people adapted to the circumstances and their "new normal." Our linguistic analyses revealed that people expressed mental health concerns regarding personal and professional challenges, health care and precautionary measures, and pandemic-related awareness. This study shows the potential to provide insights to mental health care and stakeholders and policy makers in planning and implementing measures to mitigate mental health risks amid the health crisis.

Effective use of social media platforms for promotion of mental health awareness

BACKGROUND

Social media platforms are progressively developing as a rich source of mass communication. Increasing mental health awareness with the help of social media can be a good initiative to reach out to a large number of people in a short time frame. This study was conducted to understand the usefulness of social media platforms for health promotion.

MATERIALS AND METHODS

It was a qualitative study to evaluate the effectiveness of social media platforms in hosting health promotion campaigns in the field of mental health, which was observed over 5 months from May to September 2019 to reach more people for effective information dissemination. The campaigns were as follows (1) The Buddies for Suicide Prevention: an online campaign to create awareness about suicide prevention. The campaign included script writing, slogan writing, poster making, and short films making, organized for the general public who were interested to take part; (2) The #Iquitobacco was a 21-day campaign with an idea of tobacco cessation in the community, conducted among social media viewers who were willing to participate; and (3) #Migrainethepainfultruth was yet another campaign conducted among the social media viewers who were interested to participate. All the campaigns were conducted using two famous social media platforms commonly used by young adults. Descriptive statistics such as frequency and proportions were computed for the number of likes and shares.

RESULTS

The Facebook and Instagram posts concerning all the campaigns brought about a considerable amount of reach to the targeted population. After the campaigns, the page reached to around 10.3 k people (both fans and nonfans).

CONCLUSIONS

Use of social media to conduct mental health campaigns is an effective initiative as one can reach out to several people over a short time period. There is an increasing trend in the awareness of mental health with the effective use of digital media as a platform for disseminating information.

Infodemiology and Infoveillance: Scoping Review

BACKGROUND

Web-based sources are increasingly employed in the analysis, detection, and forecasting of diseases and epidemics, and in predicting human behavior toward several health topics. This use of the internet has come to be known as infodemiology, a concept introduced by Gunther Eysenbach. Infodemiology and infoveillance studies use web-based data and have become an integral part of health informatics research over the past decade.

OBJECTIVE

The aim of this paper is to provide a scoping review of the state-of-the-art in infodemiology along with the background and history of the concept, to identify sources and health categories and topics, to elaborate on the validity of the employed methods, and to discuss the gaps identified in current research.

METHODS

The PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines were followed to extract the publications that fall under the umbrella of infodemiology and infoveillance from the JMIR, PubMed, and Scopus databases. A total of 338 documents were extracted for assessment.

RESULTS

Of the 338 studies, the vast majority (n=282, 83.4%) were published with JMIR Publications. The Journal of Medical Internet Research features almost half of the publications (n=168, 49.7%), and JMIR Public Health and Surveillance has more than one-fifth of the examined studies (n=74, 21.9%). The interest in the subject has been increasing every year, with 2018 featuring more than one-fourth of the total publications (n=89, 26.3%), and the publications in 2017 and 2018 combined accounted for more than half (n=171, 50.6%) of the total number of publications in the last decade. The most popular source was Twitter with 45.0% (n=152), followed by Google with 24.6% (n=83), websites and platforms with 13.9% (n=47), blogs and forums with 10.1% (n=34), Facebook with 8.9% (n=30), and other search engines with 5.6% (n=19). As for the subjects examined, conditions and diseases with 17.2% (n=58) and epidemics and outbreaks with 15.7% (n=53) were the most popular categories identified in this review, followed by health care (n=39, 11.5%), drugs (n=40, 10.4%), and smoking and alcohol (n=29, 8.6%).

CONCLUSIONS

The field of infodemiology is becoming increasingly popular, employing innovative methods and approaches for health assessment. The use of web-based sources, which provide us with information that would not be accessible otherwise and tackles the issues arising from the time-consuming traditional methods, shows that infodemiology plays an important role in health informatics research.

Efficacy of Instagram in Promoting Psychoeducation in the Chinese-Speaking Population

Purpose: To evaluate the efficacy of the use of Instagram in disseminating information regarding first-episode psychosis and schizophrenia.

Methods: Facebook and Instagram advertisements linked to an external YouTube video detailing first-time psychosis were initiated for 48 h. Metrics regarding the number of unique individuals reached and number of engagements were collected. Descriptive statistics were used to analyze the data.

Results: Facebook made 85 impressions (32.82%) and Instagram made 174 impressions (67.18%). Facebook had 24 engagements, whereas Instagram had 42.

Conclusion: Instagram is noninferior to Facebook in disseminating psychoeducational material to the Chinese-speaking population.

A computational study of mental health awareness campaigns on social media

As public discourse continues to progress online, it is important for mental health advocates, public health officials, and other curious parties and stakeholders, ranging from researchers, to those affected by the issue, to be aware of the advancing new mediums in which the public can share content ranging from useful resources and self-help tips to personal struggles with respect to both illness and its stigmatization. A better understanding of this new public discourse on mental health, often framed as social media campaigns, can help perpetuate the allocation of sparse mental health resources, the need for educational awareness, and the usefulness of community, with an opportunity to reach those seeking help at the right moment. The objective of this study was to understand the nature of and engagement around mental health content shared on mental health campaigns, specifically #MyTipsForMentalHealth on Twitter around World Mental Health Awareness Day in 2017. We collected 14,217 Twitter posts from 10,805 unique users between September and October 2017 that contained the hashtag #MyTipsForMentalHealth. With the involvement of domain experts, we hand-labeled 700 posts and categorized them as (a) Fact, (b) Stigmatizing, (c) Inspirational, (d) Medical/Clinical Tip, (e) Resource Related, (f) Lifestyle or Social Tip or Personal View, and (g) Off Topic. After creating a "seed" machine learning classifier, we used both unsupervised and semi supervised methods to classify posts into the various expert identified topical categories. We also performed a content analysis to understand how information on different topics spread through social networks. Our support vector machine classification algorithm achieved a mean cross-validation accuracy of 0.81 and accuracy of 0.64 on unseen data. We found that inspirational Twitter posts were the most spread with a mean of 4.17 retweets, and stigmatizing content was second with a mean of 3.66 retweets. Classification of social media-related mental health interactions offers valuable insights on public sentiment as well as a window into the evolving world of online self-help and the varied resources within. Our results suggest an important role for social media-based peer support to not only guide information seekers to useful content and local resources but also illuminate the socially-insular aspects of stigmatization. However, our results also reflect the challenges of quantifying the heterogeneity of mental health content on social media and the need for novel machine learning methods customized to the challenges of the field.

Attracting Users to Online Health Communities: Analysis of LungCancer.net's Facebook Advertisement Campaign Data

BACKGROUND

With growing numbers of adults turning to the internet to get answers for health-related questions, online communities provide platforms with participatory networks to deliver health information and social support. However, to optimize the benefits of these online communities, these platforms must market effectively to attract new members and promote community growth.

OBJECTIVE

The aim of this study was to assess the engagement results of Facebook advertisements designed to increase membership in the LungCancer.net online community.

METHODS

In the fall of 2017, a series of 5 weeklong Facebook advertisement campaigns were launched targeting adults over the age of 18 years with an interest in lung cancer to increase opt ins to the LungCancer.net community (ie, the number of people who provided their email to join the site).

RESULTS

The advertisements released during this campaign had a sum reach of 91,835 people, and 863 new members opted into the LungCancer.net community by providing their email address. Females aged 55 to 64 years were the largest population reached by the campaign (31,401/91,835; 34.29%), whereas females aged 65 and older were the largest population who opted into the LungCancer.net community (307/863; 35.57%). A total of US $1742 was invested in the Facebook campaigns, and 863 people opted into LungCancer.net, resulting in a cost of US $2.02 per new member.

CONCLUSIONS

This research demonstrates the feasibility of using Facebook advertising to promote and grow online health communities. More research is needed to compare the effectiveness of various advertising approaches. Public health professionals should consider Facebook campaigns to effectively connect intended audiences to health information and support.

A Social Media Study on the Effects of Psychiatric Medication Use

Understanding the effects of psychiatric medications during mental health treatment constitutes an active area of inquiry. While clinical trials help evaluate the effects of these medications, many trials suffer from a lack of generalizability to broader populations. We leverage social media data to examine psychopathological effects subject to self-reported usage of psychiatric medication. Using a list of common approved and regulated psychiatric drugs and a Twitter dataset of 300M posts from 30K individuals, we develop machine learning models to first assess effects relating to mood, cognition, depression, anxiety, psychosis, and suicidal ideation. Then, based on a stratified propensity score based causal analysis, we observe that use of specific drugs are associated with characteristic changes in an individual's psychopathology. We situate these observations in the psychiatry literature, with a deeper analysis of pre-treatment cues that predict treatment outcomes. Our work bears potential to inspire novel clinical investigations and to build tools for digital therapeutics.

Areas of Interest and Stigmatic Attitudes of the General Public in Five Relevant Medical Conditions: Thematic and Quantitative Analysis Using Twitter

BACKGROUND

Twitter is an indicator of real-world performance, thus, is an appropriate arena to assess the social consideration and attitudes toward psychosis.

OBJECTIVE

The aim of this study was to perform a mixed-methods study of the content and key metrics of tweets referring to psychosis in comparison with tweets referring to control diseases (breast cancer, diabetes, Alzheimer, and human immunodeficiency virus).

METHODS

Each tweet's content was rated as nonmedical (NM: testimonies, health care products, solidarity or awareness and misuse) or medical (M: included a reference to the illness's diagnosis, treatment, prognosis, or prevention). NM tweets were classified as positive or pejorative. We assessed the appropriateness of the medical content. The number of retweets generated and the potential reach and impact of the hashtags analyzed was also investigated.

RESULTS

We analyzed a total of 15,443 tweets: 8055 classified as NM and 7287 as M. Psychosis-related tweets (PRT) had a significantly higher frequency of misuse 33.3% (212/636) vs 1.15% (853/7419; P<.001) and pejorative content 36.2% (231/636) vs 11.33% (840/7419; P<.001). The medical content of the PRT showed the highest scientific appropriateness 100% (391/391) vs 93.66% (6030/6439; P<.001) and had a higher frequency of content about disease prevention. The potential reach and impact of the tweets related to psychosis were low, but they had a high retweet-to-tweet ratio.

CONCLUSIONS

We show a reduced number and a different pattern of contents in tweets about psychosis compared with control diseases. PRT showed a predominance of nonmedical content with increased frequencies of misuse and pejorative tone. However, the medical content of PRT showed high scientific appropriateness aimed toward prevention.

Internet Searches for Medical Symptoms Before Seeking Information on 12-Step Addiction Treatment Programs: A Web-Search Log Analysis

Background

Brief intervention is a critical method for identifying patients with problematic substance use in primary care settings and for motivating them to consider treatment options. However, despite considerable evidence of delay discounting in patients with substance use disorders, most brief advice by physicians focuses on the long-term negative medical consequences, which may not be the best way to motivate patients to seek treatment information.

Objective

Identification of the specific symptoms that most motivate individuals to seek treatment information may offer insights for further improving brief interventions. To this end, we used anonymized internet search engine data to investigate which medical conditions and symptoms preceded searches for 12-step meeting locators and general 12-step information.

Methods

We extracted all queries made by people in the United States on the Bing search engine from November 2016 to July 2017. These queries were filtered for those who mentioned seeking Alcoholics Anonymous (AA) or Narcotics Anonymous (NA); in addition, queries that contained a medical symptom or condition or a synonym thereof were analyzed. We identified medical symptoms and conditions that predicted searches for seeking treatment at different time lags. Specifically, symptom queries were first determined to be significantly predictive of subsequent 12-step queries if the probability of querying a medical symptom by those who later sought information about the 12-step program exceeded the probability of that same query being made by a comparison group of all other Bing users in the United States. Second, we examined symptom queries preceding queries on the 12-step program at time lags of 0-7 days, 7-14 days, and 14-30 days, where the probability of asking about a medical symptom was greater in the 30-day time window preceding 12-step program information-seeking as compared to all previous times that the symptom was queried.

Results

In our sample of 11,784 persons, we found 10 medical symptoms that predicted AA information seeking and 9 symptoms that predicted NA information seeking. Of these symptoms, a substantial number could be categorized as nonsevere in nature. Moreover, when medical symptom persistence was examined across a 1-month time period, a substantial number of nonsevere, yet persistent, symptoms were identified.

Conclusions

Our results suggest that many common or nonsevere medical symptoms and conditions motivate subsequent interest in AA and NA programs. In addition to highlighting severe long-term consequences, brief interventions could be restructured to highlight how increasing substance misuse can worsen discomfort from common medical symptoms in the short term, as well as how these worsening symptoms could exacerbate social embarrassment or decrease physical attractiveness.

Using Facebook in Recruiting Kidney Transplant Recipients for a REDCap Study

This web-based study recruited kidney transplant recipients from Facebook using three recruiting methods over a 5-week period. Participants completed 125 survey items via REDCap (Research Electronic Data Capture) survey. Facebook recruitment generated 153 eligible participants who completed surveys. The average survey response time was 15.07 min (SD = 6.12; range: 4-43), with a low missing item rate (<5%). Facebook's standard ads were most effective for recruiting subjects (n = 78, 51%), followed by three targeted Facebook kidney transplant support groups (n = 52, 34%) and a pay-to-promote study page (n = 12, 7.8%). The average cost paid for each valid survey was US$2.19 through standard Facebook ads and US$2.92 from the study page. The cost for online survey completion is economically feasible even for those with limited funds. Issues related to online surveys including extreme survey response times and participant misrepresentation were reported in this study.

Enhanced Molecular Appreciation of Psychiatric Disorders Through High-Dimensionality Data Acquisition and Analytics

The initial diagnosis, molecular investigation, treatment, and posttreatment care of major psychiatric disorders (schizophrenia and bipolar depression) are all still significantly hindered by the current inability to define these disorders in an explicit molecular signaling manner. High-dimensionality data analytics, using large datastreams from transcriptomic, proteomic, or metabolomic investigations, will likely advance both the appreciation of the molecular nature of major psychiatric disorders and simultaneously enhance our ability to more efficiently diagnose and treat these debilitating conditions. High-dimensionality data analysis in psychiatric research has been heterogeneous in aims and methods and limited by insufficient sample sizes, poorly defined case definitions, methodological inhomogeneity, and confounding results. All of these issues combine to constrain the conclusions that can be extracted from them. Here, we discuss possibilities for overcoming methodological challenges through the implementation of transcriptomic, proteomic, or metabolomics signatures in psychiatric diagnosis and offer an outlook for future investigations. To fulfill the promise of intelligent high-dimensionality data-based differential diagnosis in mental disease diagnosis and treatment, future research will need large, well-defined cohorts in combination with state-of-the-art technologies.

Monitoring Online Discussions About Suicide Among Twitter Users With Schizophrenia: Exploratory Study

BACKGROUND

People with schizophrenia experience elevated risk of suicide. Mental health symptoms, including depression and anxiety, contribute to increased risk of suicide. Digital technology could support efforts to detect suicide risk and inform suicide prevention efforts.

OBJECTIVE

This exploratory study examined the feasibility of monitoring online discussions about suicide among Twitter users who self-identify as having schizophrenia.

METHODS

Posts containing the terms suicide or suicidal were collected from a sample of Twitter users who self-identify as having schizophrenia (N=203) and a random sample of control users (N=173) over a 200-day period. Frequency and timing of posts about suicide were compared between groups. The associations between posting about suicide and common mental health symptoms were examined.

RESULTS

Twitter users who self-identify as having schizophrenia posted more tweets about suicide (mean 7.10, SD 15.98) compared to control users (mean 1.89, SD 4.79; t₃₇₄=-4.13, P<.001). Twitter users who self-identify as having schizophrenia showed greater odds of tweeting about suicide compared to control users (odds ratio 2.15, 95% CI 1.42-3.28). Among all users, tweets about suicide were associated with tweets about depression (r=0.62, P<.001) and anxiety (r=0.45, P<.001).

CONCLUSIONS

Twitter users who self-identify as having schizophrenia appear to commonly discuss suicide on social media, which is associated with greater discussion about other mental health symptoms. These findings should be interpreted cautiously, as it is not possible to determine whether online discussions about suicide correlate with suicide risk. However, these patterns of online discussion may be indicative of elevated risk of suicide observed in this patient group. There may be opportunities to leverage social media for supporting suicide prevention among individuals with schizophrenia.

Searching the Internet for psychiatric disorders among Arab and Jewish Israelis: insights from a comprehensive infodemiological survey

Israel represents a complex and pluralistic society comprising two major ethno-national groups, Israeli Jews and Israeli Arabs, which differ in terms of religious and cultural values as well as social constructs. According to the so-called “diversification hypothesis”, within the framework of e-health and in the era of new information and communication technologies, seeking online health information could be a channel to increase health literacy, especially among disadvantaged groups. However, little is known concerning digital seeking behavior and, in particular, digital mental health literacy. This study was conducted in order to fill in this gap. Concerning raw figures, unadjusted for confounding variables (time, population size, Internet penetration index, disease rate), “depression” searched in Hebrew was characterized by 1.5 times higher search volumes, slightly declining throughout time, whereas relative search volumes (RSVs) related to “depression” searched in Arabic tended to increase over the years. Similar patterns could be detected for “phobia” (in Hebrew 1.4-fold higher than in Arabic) and for “anxiety” (with the searches performed in Hebrew 2.3 times higher than in Arabic). “Suicide” in Hebrew was searched 2.0-fold more than in Arabic (interestingly for both languages search volumes exhibited seasonal cyclic patterns). Eating disorders were searched more in Hebrew: 8.0-times more for “bulimia”, whilst “anorexia” was searched in Hebrew only. When adjusting for confounding variables, association between digital seeking behavior and ethnicity remained statistically significant (p-value < 0.0001) for all psychiatric disorders considered in the current investigation, except for “bulimia” (p = 0.989). More in details, Israeli Arabs searched for mental health disorders less than Jews, apart from “depression”. Arab and Jewish Israelis, besides differing in terms of language, religion, social and cultural values, have different patterns of usage of healthcare services and provisions, as well as e-healthcare services concerning mental health. Policy- and decision-makers should be aware of this and make their best efforts to promote digital health literacy among the Arab population in Israel.

Toward Impactful Collaborations on Computing and Mental Health

We describe an initiative to bring mental health researchers, computer scientists, human-computer interaction researchers, and other communities together to address the challenges of the global mental ill health epidemic. Two face-to-face events and one special issue of the Journal of Medical Internet Research were organized. The works presented in these events and publication reflect key state-of-the-art research in this interdisciplinary collaboration. We summarize the special issue articles and contextualize them to present a picture of the most recent research. In addition, we describe a series of collaborative activities held during the second symposium and where the community identified 5 challenges and their possible solutions.