The Age of Social Sensing

Abstract

Online social media, such as Twitter and Instagram, democratized information broadcast, allowing anyone to share information about themselves and their surroundings at an unprecedented scale. The large volume of information thus posted on these media offer a new lens into the physical world through the eyes of the social network. The exploitation of this lens to inspect aspects of world state has recently been termed social sensing. The power of manipulating reality via the use (or intentional misuse) of social media opened concerns with issues ranging from radicalization by terror propaganda to potential manipulation of elections in mature democracies. Many important challenges and open research questions arise in this emerging field that aims to better understand how information can be extracted from the medium and what properties characterize the extracted information and the world it represents. Addressing the above challenges requires multi-disciplinary research at the intersection of computer science and social sciences that combines cyber-physical computing, sociology, sensor networks, social networks, cognition, data mining, estimation theory, data fusion, information theory, linguistics, machine learning, behavioral economics, and possibly others. This paper surveys important directions in social sensing, identifies current research challenges, and outlines avenues for future research.

Towards reliable online clickbait video detection: A content-agnostic approach

“…A possible solution is to leverage the social media bot detection techniques [45] and train a classifier that can discriminate comments generated by humans and bots. Meanwhile, we can also adopt the truth discovery [21] and fact-checking [46] approaches to verify the truthfulness of the user comments. The authors believe the above extensions will further improve the effectiveness and robustness of the OVCP scheme in detecting online clickbait videos.…”

An online reinforcement learning approach to quality-cost-aware task allocation for multi-attribute social sensing

“…Social sensing has emerged as a new application paradigm due to the proliferation of portable devices and ubiquitous Internet connectivity [2]. A recent survey of social sensing can be found in [1]. Social sensing has been widely used in environment sensing [12], traffic monitoring [22], emergence and disaster response [23], social sensor profiling [24], point-of-interest (POI) identification [25], clickbait video detection [26], and abnormal event identification [27].…”

“…This paper presents an online reinforcement learning framework to solve the quality-cost-aware task allocation problem in multi-attribute social sensing applications. Social sensing has emerged as a new sensing paradigm in pervasive and mobile computing applications where humans (or devices on their behalf) collectively report measurements about the physical world [1,2]. Examples of social sensing applications include air quality and environment monitoring in smart cities using mobile devices [3], malfunctioning urban infrastructures reporting using geotagging [4], and damage assessment in disaster response using online social media [5].…”

The Future of Deception: Machine-Generated and Manipulated Images, Video, and Audio?

“…Social sensing techniques were designed for analyzing unreliable data [1], but not explicitly built for adversarial generated and manipulated data. The adversarial use of social media to spread deceptive or misleading information poses a social, economic, and political threat [2].…”