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How Artificial Intelligence Uses Social Media Data to Machine Humanity

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In the process of artificial intelligence development, AI+ all big data seems to be able to increase efficiency and get a good result. As a matter of course, the combination of social media data and AI will have a negative impact.

We are used to expressing emotions, expressing interests and hobbies in social media, and even inadvertently revealing our personal information. As an ordinary person, even if you know that this is an open space, you will not have the impression of data leakage. After all, most of the information display weights of social media are based on the popularity of information, and individual users’ ordinary speeches are difficult to be unfamiliar. People deliberately see.

But with the super power and data analysis capabilities that AI brings, everything becomes different. After crawling data, AI+ social media data can see more than just one person’s reaction, emotion and expression to things, but can see information about a whole group or even a whole ethnic group. The previous series of information disclosures that almost ran through Facebook’s doorway revealed how much of the scattered personal information that AI might have had.

However, social media data is not only dark, but there are already many neurological and psychological studies that have begun to use AI and social media data to use machines to deal with human nature.

“Twitter big data tells you that people around the world are the same”

Recently, the University of Bristol used machine learning to analyze 800 million tweets in 57 cities in the UK within four years and reached a conclusion that we have long pondered about. Humans generally have high emotions in the morning and low emotions in the night.

The whole analysis process is like this. The research team sampled through the Twitter search API and collected 800 million tweets. All the # topics, emojis, holiday greetings, etc. are washed away, and the words are tagged according to the psychometric method.

For example: emotional positive emotions and negative emotions; time-oriented attention now, attention to the past, attention to the future; personal attention to work, family, money, society, religion, and so on.

With this strictly machine-learning model based on the dimensions of psychological research, the research on social media data will be more specialized, rather than simply relying on NLP to analyze the emotions in the language.

The conclusion of the final study is: In the 24 hours of the day, human emotions will not only change, thinking patterns will also change.

From the 5-6 pm of the day, people began to enter the peak period of expression in social media, and at this time people’s emotional expression is more positive, and the focus is also more on the individual status. As time goes by from 7-9, people’s emotions tend to be biased towards anger, but if it is on non-working days, this positive and enjoyable state will continue.

At this time, people’s thinking patterns tend to be class-thinking, thinking is more clear and direct, logical, and stereotyped tendencies appear.

At night, people’s emotional expression will turn negative, and the focus will shift from individuals to society. As time goes on, the closer to the next day’s 3-4 a.m., the more people focus their attention on religion. In this period of time, people’s thinking patterns tend to be existentialism, which reflects the state of confusion, anxiety, irrationality, and willingness to participate and share.

In plain words, one’s general state is to rise in the morning with enthusiasm and self-confidence, to plan his own life in the blood of chickens, and to wait until the evening when he starts to fall into a state of whimper, paying attention to every corner of the world. People who are sad or touched, if they can’t sleep at night, begin to seek religious salvation. Look, is this process the same for Chinese foreigners?

When social media becomes a research assistant in psychology, can it be diagnosed from a selfie?

In fact, the time-cycle changes in human emotions have long been confirmed. Because of the physiological causes of nerve fatigue, melatonin secretion, etc., our emotions will show different states during the day.

Although this study of social media big data just confirmed this change again and did not uncover more reasons for emotional change, it is the first time that the relationship between mood cycles and thinking patterns has changed. In fact, there are many researches on social media data and psychology, and many interesting information have been discovered.

For example, last year, the University of Pittsburgh conducted a survey to investigate the social media use of depression patients. The results showed that the average duration of social media usage for depression patients was much higher than that of ordinary people.

Harvard University research shows that people with depression prefer to use cool, faded, or black-and-white filters when they publish photos on social media.

Unbreakable Ethics Levels

At present, the role of social media data for psychology seems to remain in academic research. Can we see the psychological application of social media data in our lifetime?

At present, social media data has at least the following applications for psychology:

1.As an aid to the measurement of mental state

In addition to those mental illnesses that directly lead to hallucinations, insomnia, and other direct manifestations, there are many types or degrees of mental illness that are difficult to objectively feel. Most of the time, it is necessary to rely on the face-to-face consultation or to fill in the psychological state measurement table to confirm, but the patient may not be able to directly show his or her true state when he or she fills out independently. At this time, information undoubtedly revealed in social media can be used as a support.

2.The psychological status of the group

Compared with the individual’s psychological problems, the more complicated situation is experienced by an entire group. For example: changes in mental state that can occur when disasters or accidents occur.

For example, employee/student suicide occurs in a company or school, or an entire region suffers serious natural disasters such as earthquakes and typhoons. At this time, we often do not have the energy to do psychological counseling for everyone, and there is no way to assess the psychological status of the group as a whole. At most, the group conducts psychological counseling in the form of group lessons.

At this time, using machine learning to research social media data, you can clearly see the group’s psychological response to events. Even the long-term psychological status tracking of the crowd, and selective, targeted psychological counseling.

HIT has proposed a method to identify college students’ social media data by establishing classifiers to identify the risk of depression.

In fact, the application methods mentioned above are hardly technically difficult to achieve. Although the results obtained may not always be absolutely accurate, the value that can be provided for psychology, a labor-intensive industry, is very small.

But the biggest issue is whether it is ethical. Should publicly released social media data be considered personal privacy? The information extracted from it is not considered personal privacy? Even if it is a patient with mental illness, citizens should have the right not to disclose their prevalence, and to discover the citizens’ mental health status through social media data. Is this a serious violation of this power? In particular, if this technology is applied to colleges and universities, will anyone be so concerned that the teachers and classmates around them have learned their psychological state and have made their mental condition worse?

In fact, to a certain extent, we sometimes deliberately choose some ineffective solutions to problems, but we can exchange security and freedom for the soul.

The author has an experience of more than 6 years of corporate experience in various technology platforms such as Big Data, AWS, Data Science, Artificial Intelligence, Machine Learning, Blockchain, Python, SQL, JAVA, Oracle, Digital Marketing etc. He is a technology nerd and loves contributing to various open platforms through blogging. He is currently in association with a leading professional training provider, Mindmajix Technologies INC. and strives to provide knowledge to aspirants and professionals through personal blogs, research, and innovative ideas.

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From nanotechnology to solar power: Solutions to drought

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While the drought has intensified in Iran and the country is facing water stress, various solutions from the use of solar power plants to the expansion of watershed management and nanotechnology are offered by experts and officials.

Iran is located in an arid and semi-arid region, and Iranians have long sought to make the most of water.

In recent years, the drought has intensified making water resources fragile and it can be said that we have reached water bankruptcy in Iran.

However, water stress will continue this fall (September 23-December 21), and the season is expected to be relatively hot and short of rain, according to Ahad Vazifeh, head of the national center for drought and crisis management.

In such a situation, officials and experts propose various solutions for optimal water management.

Alireza Qazizadeh, a water and environment expert, referring to 80 percent of the arid regions in the country, said that “Iran has one percent of the earth’s area and receives only 36 percent of renewable resources.

The country receives 250 mm of rainfall annually, which is about 400 billion cubic meters, considering 70 percent evaporation, there is only 130 billion cubic meters of renewable water and 13 billion cubic meters of input from border waters.”

Referring to 800 ml of average rainfall and 700 mm of global evaporation, he noted that 70 percent of rainfall in Iran occurs in only 25 percent of the country and only 25 percent rains in irrigation seasons.

Pointing to the need for 113 billion cubic meters of water in the current year (began on March 21), he stated that “of this amount, 102 billion is projected for agricultural use, 7 percent for drinking and 2 percent for industry, and at this point water stress occurs.

In 2001, 5.5 billion cubic meters of underground resources were withdrawn annually, and if we consider this amount as 20 years from that year until now, it means that we have withdrawn an equivalent of one year of water consumption from non-renewable resources, which is alarming.”

The use of unconventional water sources can be effective in controlling drought, such as rainwater or river runoff, desalinated water, municipal wastewater that can be reused by treatment, he concluded.

Rasoul Sarraf, the Faculty of Materials at Shahid Modarres University, suggests a different solution and states that “To solve ease water stress, we have no choice but to use nanotechnology and solar power plants.

Pointing to the sun as the main condition for solar power plant, and while pointing to 300 sunny days in the country, he said that at the Paris Convention, Iran was required to reduce emissions by 4 percent definitively and 8 percent conditionally, which will only be achieved by using solar power plants.

Hamidreza Zakizadeh, deputy director of watershed management at Tehran’s Department of Natural Resources and Watershed Management, believes that watershed management can at least reduce the effects of drought by managing floods and extracting water for farmers.

Amir Abbas Ahmadi, head of habitats and regional affairs of Tehran Department of Environment, also referring to the severe drought in Tehran, pointed to the need to develop a comprehensive plan for water management and said that it is necessary to cooperate with several responsible bodies and develop a comprehensive plan to control the situation.

He also emphasizes the need to control migration to the capital, construction, and the implementation of the Comprehensive Plan of Tehran city.

While various solutions are proposed by officials and experts to manage water and deal with drought, it is necessary for the related organizations to work together to manage the current situation.

Mohammad Reza Espahbod, an expert in groundwater resources, also suggested that while the country is dealing with severe drought due to improper withdrawal of groundwater and low rainfall, karst water resources can supply the whole water needed by the country, only if managed.

Iran is the fifth country in the world in terms of karst water resources, he stated.

Qanats can also come efficient to contain water scarcity due to relatively low cost, low evaporation rates, and not requiring technical knowledge, moreover, they proved sustainable being used in perpetuity without posing any damages to the environment.

According to the Ministry of Energy, about 36,300 qanats have been identified in Iran, which has been saturated with water for over 2,000 years.

In recent years, 3,800 qanats have been rehabilitated through watershed and aquifer management, and people who had migrated due to water scarcity have returned to their homes.

Water resources shrinking

Renewable water resources have decreased by 30 percent over the last four decades, while Iran’s population has increased by about 2.5 times, Qasem Taqizadeh, deputy minister of energy, said in June.

The current water year (started on September 23, 2020) has received the lowest rain in the past 52 years, so climate change and Iran’s arid region should become a common belief at all levels, he lamented.

A recent report by Nature Scientific Journal on Iran’s water crisis indicates that from 2002 to 2015, over 74 billion cubic meters have been extracted from aquifers, which is unprecedented and its revival takes thousands of years along with urgent action.

Three Iranian scientists studied 30 basins in the country and realized that the rate of aquifer depletion over a 14-year period has been about 74 billion cubic meters, which is recently published in Nature Scientific Journal.

Also, over-harvesting in 77 percent of Iran has led to more land subsidence and soil salinity. Research and statistics show that the average overdraft from the country’s aquifers was about 5.2 billion cubic meters per year.

Mohammad Darvish, head of the environment group in the UNESCO Chair on Social Health, has said that the situation of groundwater resources is worrisome.

From our partner Tehran Times

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Technology and crime: A never-ending cat-and-mouse game

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Is technology a good or bad thing? It depends on who you ask, as it is more about the way technology is used. Afterall, technology can be used by criminals but can also be used to catch criminals, creating a fascinating cat-and-mouse game.

Countless ways technology can be used for evil

The first spear was used to improve hunting and to defend from attacking beasts. However, it was also soon used against other humans; nuclear power is used to produce energy, but it was also used to annihilate whole cities. Looking at today’s news, we’ve learned that cryptocurrencies could be (and are) used as the preferred form of payments of ransomware since they provide an anonymous, reliable, and fast payment method for cybercriminals.

Similarly, secure phones are providing criminal rings with a fast and easy way to coordinate their rogue activities. The list could go on. Ultimately, all technological advancements can be used for good or evil. Indeed, technology is not inherently bad or good, it is its usage that makes the difference. After all, spears served well in preventing the extinction of humankind, nuclear power is used to generate energy, cryptocurrency is a promise to democratize finance, and mobile phones are the device of choice of billions of people daily (you too are probably reading this piece on a mobile).

However, what is new with respect to the past (recent and distant) is that technology is nowadays much more widespread, pervasive, and easier to manipulate than it was some time ago. Indeed, not all of us are experts in nuclear material, or willing and capable of effectively throwing a spear at someone else. But each of us is surrounded by, and uses, technology, with a sizeable part of users also capable of modifying that technology to better serve their purposes (think of computer scientists, programmers, coding kids – technology democratization).

This huge reservoir of people that are capable of using technology in a way that is different from what it was devised for, is not made of just ethical hackers: there can be black hats as well (that is, technology experts supporting evil usages of such technology). In technical terms, the attack vector and the security perimeter have dramatically expanded, leading to a scenario where technology can be easily exploited for rogue purposes by large cohorts of people that can attack some of the many assets that are nowadays vulnerable – the cybersecurity domain provides the best example for the depicted scenario. 

Fast-paced innovation and unprecedented threats

What is more, is that technology developments will not stop. On the contrary, we are experiencing an exponentially fast pace in technology innovation, that resolves in less time between technology innovations cycles that, while improving our way of living, also pave the way for novel, unprecedented threats to materialize. For instance, the advent of quantum computers will make the majority of current encryption and digital signature methods useless and what was encrypted and signed in the past, exposed.

The tension between legitimate and illegitimate usages of technology is also heating up. For instance, there are discussions in the US and the EU about the need for the provider of ICT services to grant the decryption keys of future novel secure applications to law enforcement agencies should the need arise –a debatable measure.

However, technology is the very weapon we need to fight crime. Think of the use of Terahertz technology to discover the smuggling of drugs and explosives – the very same technology Qatar      has successfully employed. Or the infiltration of mobile phone crime rings by law enforcement operators via high tech, ethical hacking (as it was the case for the EncroChat operation). And even if crime has shown the capability to infiltrate any sector of society, such as sports, where money can be laundered over digital networks and matches can be rigged and coordinated via chats, technology can help spot the anomalies of money transfer, and data science can spot anomalies in matches, and can therefore thwart such a crime – a recent United Nations-sponsored event, participated by the International Centre for Sport Security (ICSS) Qatar and the College of Science and Engineering (CSE) at Hamad Bin Khalifa University (HBKU) discussed      the cited topic. In the end, the very same technology that is used by criminals is also used to fight crime itself.

Don’t get left behind

In the above-depicted cybersecurity cat-and-mouse game, the loser is the party that does not update its tools, does not plan, and does not evolve.

In particular, cybersecurity can help a country such as Qatar over two strategic dimensions: to better prevent/detect/react to the criminal usage of technology, as well as to advance robustly toward a knowledge-based economy and reinforce the country’s presence in the segment of high value-added services and products to fight crime.

In this context, a safe bet is to invest in education, for both governments and private citizens. On the one hand, only an educated workforce would be able to conceptualize/design/implement advanced cybersecurity tools and frameworks, as well as strategically frame the fight against crime. On the other hand, the same well-educated workforce will be able to spur innovation, create start-ups, produce novel high-skill products, and diversify the economy. 

In this context, Qatar enjoys a head start, thanks to its huge investment in education over the last 20 years. In particular, at HBKU – part of Qatar Foundation – where we have been educating future generations. 

CSE engages and leads in research disciplines of national and global importance. The college’s speciality divisions are firmly committed to excellence in graduate teaching and training of highly qualified students with entrepreneurial  capacity.

For instance, the MS in Cybersecurity offered by CSE touches on the foundations of cryptocurrencies, while the PhD in Computer Science and Engineering, offering several majors (including cybersecurity), prepares future high-level decision-makers, researchers, and entrepreneurs in the ICT domain  – the leaders who will be driving the digitalization of the economy and leading the techno-fight against crime. 

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Enhancing poverty measurement through big data

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Authors: Jasmina Ernst and Ruhimat Soerakoesoemah*

Ending poverty in all its forms is the first of the 17 Sustainable Development Goals (SDGs). While significant progress to reduce poverty had been made at the global and regional levels by 2019, the Covid-19 pandemic has partly reversed this trend. A significant share of the population in South-East Asia still lacks access to basic needs such as health services, proper nutrition and housing, causing many children to suffer from malnutrition and treatable illnesses. 

Delivering on the commitments of the 2030 Agenda for Sustainable Development and leaving no one behind requires monitoring of the SDG implementation trends. At the country level, national statistics offices (NSOs) are generally responsible for SDG data collection and reporting, using traditional data sources such as surveys, census and administrative data. However, as the availability of data for almost half of the SDG indicators (105 of 231) in South-East Asia is insufficient, NSOs are exploring alternative sources and methods, such as big data and machine learning, to address the data gaps. Currently, earth observation and mobile phone data receive most attention in the domain of poverty reporting. Both data sources can significantly reduce the cost of reporting, as the data collection is less time and resource intensive than for conventional data.

The NSOs of Thailand and the Philippines, with support from the Asian Development Bank, conducted a feasibility study on the use of earth observation data to predict poverty levels. In the study, an algorithm, convolutional neural nets, was pretrained on an ImageNet database to detect simple low-level features in images such as lines or curves. Following a transfer learning technique, the algorithm was then trained to predict the intensity of night lights from features in corresponding daytime satellite images. Afterwards income-based poverty levels were estimated using the same features that were found to predict night light intensity combined with nationwide survey data, register-based data, and geospatial information. The resulting machine learning models yielded an accuracy of up to 94 per cent in predicting the poverty categories of satellite images. Despite promising study results, scaling up the models and integrating big data and machine learning for poverty statistics and SDG reporting still face many challenges. Thus, NSOs need support to train their staff, gain continuous access to new datasets and expand their digital infrastructure.

Some support is available to NSOs for big data integration. The UN Committee of Experts on Big Data and Data Science for Official Statistics (UN-CEBD) oversees several task teams, including the UN Global Platform which has launched a cloud-service ecosystem to facilitate international collaboration with respect to big data. Two additional task teams focus on Big Data for the SDGs and Earth Observation data, providing technical guidance and trainings to NSOs. At the regional level, the weekly ESCAP Stats Café series provides a knowledge sharing platform for experiences related to the impact of COVID-19 on national statistical systems. The Stats Café includes multiple sessions dedicated to the use of alternative data sources for official statistics and the SDGs. Additionally, ESCAP has published policy briefs on the region’s practices in using non-traditional data sources for official statistics.

Mobile phone data can also be used to understand socioeconomic conditions in the absence of traditional statistics and to provide greater granularity and frequency for existing estimates. Call detail records coupled with airtime credit purchases, for instance, could be used to infer economic density, wealth or poverty levels, and to measure food consumption. An example can be found in poverty estimates for Vanuatu based on education, household characteristics and expenditure. These were generated by Pulse Lab Jakarta – a joint innovation facility associated with UN Global Pulse and the government of Indonesia.

Access to mobile phone data, however, remains a challenge. It requires long negotiations with mobile network operators, finding the most suitable data access model, ensuring data privacy and security, training the NSO staff and securing dedicated resources. The UN-CEBD – through the Task Team on Mobile Phone Data and ESCAP – supports NSOs in accessing and using mobile phone data through workshops, guides and the sharing of country experiences. BPS Statistics Indonesia, the Indonesian NSO, is exploring this data source for reporting on four SDG indicators and has been leading the regional efforts in South-East Asia. While several other NSOs in Asia and the Pacific can access mobile phone data or are negotiating access with mobile network operators, none of them have integrated it into poverty reporting.

As the interest and experience in the use of mobile phone data, satellite imagery and other alternative data sources for SDGs is growing among many South-East Asian NSOs, so is the need for training and capacity-building. Continuous knowledge exchange and collaboration is the best long-term strategy for NSOs and government agencies to track and alleviate poverty, and to measure the other 16 SDGs.

*Ruhimat Soerakoesoemah, Head, Sub-Regional Office for South-East Asia

UNESCAP

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