As the Internet has become prevalent, the popularity of net media has been growing, to a point that it has taken over conventional mass media. However, TWtrends, the Twitter trends visualization system operated by our research team since 2019, indicates that many topics on TV programs frequently appear on Twitter trendlines. This study investigates the relationship between Twitter and TV programs by collecting information on Twitter trends and TV programs simultaneously. Although this study provides a rough estimation of the volume of tweets that mention TV programs, the results show that several tweets mention TV programs at a constant rate, which tends to increase on the weekend. This tendency of TV-related tweets stems from the audience rating survey results. Considering the study outcome, and the fact that many TV programs introduce topics popular in social media, implies codependency between Internet media (social media) and mass media.

While social media is now used by many people and plays a role in distributing information, it has recently created an unexpected problem: the actual shrinkage of information sources. This is mainly due to the ease of connecting people with similar opinions and the recommendation system. Biased information distribution promotes polarization that divides people into multiple groups with opposing views. Also, people may receive only the seemingly positive information that they prefer, or may trigger them into holding onto their opinions more strongly when they encounter opposing views. This, combined with the characteristics of social media, is accelerating the polarization of opinions and eventually social division. In this paper, we propose a model of opinion formation on social media to simulate polarization. While based on the idea that opinion neutrality is only relative, this model provides new techniques for dealing with polarization.

With the spread of high-performance sensors and social network services (SNS) and the remarkable advances in machine learning technologies, fake media such as fake videos, spoofed voices, and fake reviews that are generated using high-quality learning data and are very close to the real thing are causing serious social problems. We launched a research project, the Media Clone (MC) project, to protect receivers of replicas of real media called media clones (MCs) skillfully fabricated by means of media processing technologies. Our aim is to achieve a communication system that can defend against MC attacks and help ensure safe and reliable communication. This paper describes the results of research in two of the five themes in the MC project: 1) verification of the capability of generating various types of media clones such as audio, visual, and text derived from fake information and 2) realization of a protection shield for media clones' attacks by recognizing them.

The 2019 Typhoon Hagibis (No. 19) caused widespread destruction in eastern Japan. During the disaster, many tweets including rescue request hashtags such as #救助 (meaning #Rescue) and #救助要請 (meaning #Rescue_request) were posted on Twitter. An official disaster information account of the Nagano Prefectural Government asked the public to provide information in the form of damage reports and rescue requests using the hashtag #台風19号長野県被害 (#Typhoon_No.19_Nagano_Prefecture_damage). As a result, many tweets were posted using this hashtag. Moreover, the account contacted the posters of tweets requesting rescue and delivered the information to the Fire Department. In this study, we analyze the circumstances of the above tweets.

Social media channels, such as Facebook, Twitter, and Instagram, have altered our world forever. People are now increasingly connected than ever and reveal a sort of digital persona. Although social media certainly has several remarkable features, the demerits are undeniable as well. Recent studies have indicated a correlation between high usage of social media sites and increased depression. The present study aims to exploit machine learning techniques for detecting a probable depressed Twitter user based on both, his/her network behavior and tweets. For this purpose, we trained and tested classifiers to distinguish whether a user is depressed or not using features extracted from his/her activities in the network and tweets. The results showed that the more features are used, the higher are the accuracy and F-measure scores in detecting depressed users. This method is a data-driven, predictive approach for early detection of depression or other mental illnesses. This study's main contribution is the exploration part of the features and its impact on detecting the depression level.

According to the official TDOAS 2009~2017 survey, the penetration rate of social media in Taiwan has reached a record 96.8%, while the Internet access rate is as high as 99.7%. However, people using government online services access to relevant information has continued to decline over the years, from 50.8% in 2009 to 35.4% in 2017. At the same time, the proportion of e-transaction users has also dropped simultaneously from 30.3% to 27.7%. In particular, only 1.1% of them are interested in government online forums, while the remaining 97.2% are more willing to engage in social media as a source of personal reference. The study aims to explore why are users not interested in accessing e-government services? Are they affected by the popularity of social networking applications? What are the key factors for users to continue to use e-government service? The research framework was adapted from expectation confirmation theory and model (ECT/ECM), technology acceptance model (TAM) with trust theories, in validating attitude measures for a better understanding of continuance intention of using e-government service. In terms of measurement, the assessment used the structural equation modeling method (SEM) to explore the views and preferences of 400 college students on e-government service. The study results identified that perceived usefulness not only plays a full mediating role, it is expected to be the most important ex-post factor influencing user's intention to continue using e-government service. It also clarifies that the intent to continue to use e-government services is not related to use any alternative means such as social media application.

Encryption-then-Compression (EtC) systems have been proposed to securely transmit images through an untrusted channel provider. In this study, EtC systems were applied to social media like Twitter that carry out image manipulations. The block scrambling-based encryption schemes used in EtC systems were evaluated in terms of their robustness against image manipulation on social media. The aim was to investigate how five social networking service (SNS) providers, Facebook, Twitter, Google+, Tumblr and Flickr, manipulate images and to determine whether the encrypted images uploaded to SNS providers can avoid being distorted by such manipulations. In an experiment, encrypted and non-encrypted JPEG images were uploaded to various SNS providers. The results show that EtC systems are applicable to the five SNS providers.

In Twitter-like services, countless messages are being posted in real-time every second all around the world. Timely knowledge about what kinds of information are diffusing in social media is quite important. For example, in emergency situations such as earthquakes, users provide instant information on their situation through social media. The collective intelligence of social media is useful as a means of information detection complementary to conventional observation. We have developed a system for monitoring and analyzing information diffusion data in real-time by tracking retweeted tweets. A tweet retweeted by many users indicates that they find the content interesting and impactful. Analysts who use this system can find tweets retweeted by many users and identify the key people who are retweeted frequently by many users or who have retweeted tweets about particular topics. However, bursting situations occur when thousands of social media messages are suddenly posted simultaneously, and the lack of machine resources to handle such situations lowers the system's query performance. Since our system is designed to be used interactively in real-time by many analysts, waiting more than one second for a query results is simply not acceptable. To maintain an acceptable query performance, we propose a capacity control method for filtering incoming tweets using extra attribute information from tweets themselves. Conventionally, there is a trade-off between the query performance and the accuracy of the analysis results. We show that the query performance is improved by our proposed method and that our method is better than the existing methods in terms of maintaining query accuracy.

Social Media has already become a new arena of our lives and involved different aspects of our social presence. Users' personal information and activities on social media presumably reveal their personal interests, which offer great opportunities for many e-commerce applications. In this paper, we propose a principled latent variable model to infer user consumption preferences at the category level (e.g. inferring what categories of products a user would like to buy). Our model naturally links users' published content and following relations on microblogs with their consumption behaviors on e-commerce websites. Experimental results show our model outperforms the state-of-the-art methods significantly in inferring a new user's consumption preference. Our model can also learn meaningful consumption-specific topics automatically.

Independent and identically distributed (i.i.d) assumptions are commonly used in the machine learning community. However, social media data violate this assumption due to the linkages. Meanwhile, with the variety of data, there exist many samples, i.e., Universum, that do not belong to either class of interest. These characteristics pose great challenges to dealing with social media data. In this letter, we fully take advantage of Universum samples to enable the model to be more discriminative. In addition, the linkages are also taken into consideration in the means of social dimensions. To this end, we propose the algorithm Semi-Supervised Linked samples Feature Selection with Universum (U-SSLFS) to integrate the linking information and Universum simultaneously to select robust features. The empirical study shows that U-SSLFS outperforms state-of-the-art algorithms on the Flickr and BlogCatalog.