Online Social Networks (OSNs), such as Facebook, Twitter, Instagram, YouTube, and TikTok, have become increasingly popular in the last decade and are now an integral part of many people’s daily lives. According to Meta reports, Facebook has 2.06 billion daily active users with an increase of 5% year-over-year and 3.03 billion monthly active users in 2023, and Facebook users spend an average of 33 minutes daily. Twitter currently has more than 237.8 million daily active users, and 556 million monthly active users. There are 500 million tweets per day, which adds up to 15 billion tweets in one month. For Instagram, there are 2 billion monthly active users and 500 million daily active users, and Instagram users spend more than 28 minutes per day on average. In addition, over 2.7 billion people access YouTube once a month, making it one of the most popular social media in the world, and YouTube Premium has reached 80 million subscribers in 2022. TikTok has 1.1 billion monthly active users, spending an average of 95 minutes on the app daily, and there are over 100 billion average monthly video views on TikTok.

National Tsing Hua University (NTHU), National Chengchi University (NCCU), and Academia Sinica jointly establish the TIGP on Social Networks and Human-Centered Computing (SNHCC) in 2012. SNHCC are new important applications and technologies that have been rapidly developing in recent years. The TIGP SNHCC program can cultivate Taiwanese and international talents in related areas to the program, strengthen innovative potential, and enhance the level of academic research. NTHU, NCCU and Academia Sinica will co-play leading and supervisory roles, and provide research resources and equipment. Additionally, the participating scholars of these three institutions will be jointly responsible for teaching activities, supervising research, and guiding international graduate students.

The TIGP on Social Networks and Human-Centered Computing is a new program established jointly by National Tsing Hua University (NTHU), National Cheng Chi University (NCCU), and Academia Sinica. Social Networks and Human-Centered Computing are new important applications and technologies that have been rapidly developed in recent years. Therefore, this TIGP can cultivate Taiwanese and international talents in the field of the industry, strengthen innovative potential, and enhance academic research level. NTHU, NCCU and Academia Sinica will co-play leading and supervising roles, and provide research resources and equipment. Additionally, the participating scholars of these three units will be jointly responsible for teaching activities, supervising research, and guiding international graduate students.

This TIGP program hopes to attract domestic and foreign outstanding young students. In the scope of social networks and human-centered computing, this program gives priority to computer science and engineering, and takes social and behavioral sciences as subsidiary to provide students with the training across multiple fields. The curriculum contents will probe into various fields, including the natural language processing and information retrieval, data mining, multimedia, human-centered computing and human-computer interaction in computer science, and sociology, communication and psychology in society and behavioral sciences. This program hopes to attract doctoral students who are interested in related fields and provide research training for them.

In TIGP on Social Networks and Human-Centered Computing, the cooperative universities and institutes will support the required laboratory equipment and instrument for teaching and research. With equipment and various expertise of researchers from cooperative institutes, this program can provide young students with a good educational opportunity to cultivate their research interests in related areas. This TIGP will focus on theory and practice to provide students with a great theoretical basis and technical capacity of solving practical problems. The degree granted is Ph.D of Social Networks and Human-Centered Computing.

Hi, my name is Lun-Wei Ku. I am the coordinator of Social Networks and Human Centered Computing, abbreviated SNHCC. This is the newest Taiwan International Graduate Program jointly hosted by Academia Sinica, National Tsing Hua University, and National Cheng-Chi University.

If you ask me, “why do we need to have a PhD program for social networks and human centered computing?” I would say it’s very easy to describe its importance. Think about google apps, smartphones, heavy facebook users. The social networks are changing our habits, languages and lives. For example, SIRI from Apple iPhone relies on the technology of Human-Centered Computing. That’s why SNHCC is founded. Our curriculum content covers Natural Language Processing, Information Retrieval, Data Mining, Social Multimedia, Mobile Social Networks and Human-centered Computing. The program also includes sociology, communication, behavior science, and others. SNHCC provides students with a solid theoretical basis and technical capability to solve practical problems.

Why come to TIGP, Academia Sincia? Well, it’s three Fs, facility, faculty, and future. We integrate the research and development activities in information technologies among various organizations in Academia Sinica, and further leverage IT-related multi-disciplinary research as well. Academia Sinica, the leading research institute in Taiwan, cooperates with two prestigious partner universities, Tsing Hua and Cheng Chi. This is TIGP, Social Networks and Human-Centered Computing, the best program you should come.

Please join us and open up your opportunities.

Natural Language Processing and Information Retrieval with Applications in Social Networks:

In information retrieval, ranking is one of the most fundamental topics. For the retrieval of articles in social networks, since social networks often record user information, such as interests and likes, which is filled by users themselves or retrieved from their written articles, the dimension “interest” can be used to improve the searching results to match user requirements.

Through natural language processing to understand inquiry meaning of users, cross-language information retrieval allows users to input questions using various languages in social network websites and obtains the information shared by other users. Also, the similarity search can further obtain similar social articles through checking in other dimensions (such as temporal).

Following the rise of location-based social networks, users can now publish their visiting places and comments in social networks, and hence, the dimension of geographical information plays a very important role on search.

Data mining in social networks:

Some websites which own considerable amount of data, e.g., the user topology of Facebook contains billions of nodes. For such social networking applications, community detection is the one of the most basic issues for mining their data. Moreover, ``Graph Pattern Mining’’ is one of the most important topics for graph data mining. In this program, the courses will include discussions from traditional graph isomorphism to frequent subgraph pattern mining, which is very important in recent years.

To deal with big data in a social graph, within limited time and computing resources, randomized approximation algorithm is valued gradually in recent years.

To avoid malice adversary obtaining users’ real identities of each corresponding node, Privacy-preserving graph mining will play a very important role in the future when social data is used in practical commercial sales.

The clustering and classification of documents in social media are also important for social networks.

Social Multimedia:

As for social multimedia, not only multimedia contents are included, but also social comments, social links, and social interactions are considered, so that the result of analysis will be more accurate and meet the requirements more precisely.

Multimedia data is linked widely in multiple dimensions, so the topic of privacy has been concerned over the recent years.

The analysis and search of music content also become a new rising research area in recent years. Many innovative research issues such as music classifications, recommendations, and watermarks are all very important.

Mobile Social Networks:

In the delay-tolerant network, when social factor is considered, the nodes can be classified effectively, and thus the close nodes can be exploited for packet delivery. Also, inside a P2P network, whether a node will deliver a package may be decided according to the data in the packet and the corresponding user’ favorite and closest degree of friends.

To avoid junk mails being passed around in social networks, a reputation model can be built, in terms of the delivery history of each node, to distinguish different nodes, and stop the nodes with good reputations from delivering data within social networks.

Investigating different networks topology structures in social networks to build topology generators for research.

Human-Centered Computing:

Setup of Human-Centered Computing, mechanism of encouragement, and how to design better Human-Computer Interface to improve the motivation of user contributions and participations in Human-Centered Computing.

For human-computer interaction, it needs to effectively collect and analyze the perception and habit of users in various computing technologies. It also requires an innovating interactive computing device to help improve user experience.

Since human behavior is complicated, and the investigation on a huge amount of users is infeasible, it is also an important topic to objectively analyze the pros and cons in experiment design.