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With the advance of science and technology, people are used to recording their daily life events via writing blogs, uploading social media posts, taking photos, or filming videos. Such rich repository personal information is useful for supporting human living assistance, such as information recall service. The main challenges are how to store and manage personal knowledge from various sources, and how to provide support for people who may have difficulty recalling past experiences. In this position paper, we propose a research agenda on personal knowledge mining from various sources of lifelogs, personal knowledge base construction, and information recall for assisting people to recall their experiences. Ten research questions are formulated.

This short paper reports from a museum innovation project using small-scale design experiments with mobile and social technologies to explore the participative museum along the Akerselva River in Oslo. We reflect upon what insights social media requires for design to engage people in participation in public and urban settings. The paper focuses on the micro-level of engagement in these media, and asks how a focus on language, semiotic and social practices may represent new possibilities for PD processes, using these media as design tools. It suggests that perspectives from cultural studies can be adapted to stage social media-based participatory design processes to reach communities that are dispersed over time and space.

Reputation of higher education institutes are significantly affected by the degree of the students' satisfactions. Therefore, considering students opinions about the quality of education is very important in order to maintain the academic institutes' reputation. Thus, the use of social media in the assessment of course quality enhances the education outcomes. Most universities explore their students' views about the quality of the offered courses using online and paper based questionnaires. However, the students' opinions in social media such as Facebook and twitters are not considered in the courses assessment of universities. This paper conduct a study on how university lectures react toward the students views throughout the social media. The contain analysis approach has been used to accomplish this study. The results showed that involving social media in courses assessments strengthen truthfulness of the courses evaluations results.

Digital backchannel systems have been proven useful for gathering real-time students' feedback in a large lecture environment. However, it is difficult for lecturers to process and respond to large amount of feedback in real time while they are teaching in a large lecture. To address this issue, we propose a mobile digital backchannel system ClasSense that allows students to give feedback during a class and extract the students' sentiments and emotions in their feedback in real time so that the lecturer can continuously monitor the morale of the student population and respond to the most important concerns students have in common. This position paper presents the key design considerations and outlines our research plan.

This short paper introduces the rationale for a workshop on computational health science, and provides a brief overview of the workshop's content. We point out some of the recent research on mining social media data for health, define what we mean by computational health science, and argue the value of meaningful multi-disciplinary collaboration.

Digitization, digital books, the web, social media, etc. all have the potential to change the way we read and write and indeed styles of reading and writing have changed. But we can also look at the ways in which such tools and possibilities have changed the way we write, produce, and read paper books. This position paper looks at the author's experiences developing a book text visualization tool for analyzing the works in Philip Pullman's children's literature trilogy, His Dark Materials, and how the prototype has prompted thinking about the way tools for different reader audiences should differ.

Research on Information Warfare on the Web is still at an early stage and the question of the true nature of Cyberwarfare actions that target the Web needs to be answered on both conceptual and methodological levels. Existing research proved that the Web is a new battlefield with specific strategic objectives, but research needs to create assessment tools to validate the impact of Cyberattacks, especially when they aim at "soft" targets like Web sites or Social Media platforms. This position paper serves as starting point of reference and discussion and wants to clarify several misunderstandings in definitions of Information Warfare and Cyberwarfare. It also offers methodological directions to identify actions and measure their impact to answer the question: Is Cyberwarfare just a weapon of mass annoyance?

In recent years, the steady emergence of digital communication, especially social media, has increased the "placelessness" of inter-person communication practices, i.e., lessening the need to reside co-located in order to communicate. When these communication practices carry over to co-located settings, they introduce redundancy and potentially even harm the co-located context, since use of personal technologies tends to isolate users from their surroundings. In this position paper, we want to raise awareness on how interactive public displays could alleviate this redundancy and potential isolation. We present a model of reinforcing co-located communications, and illustrate it through example use cases.

In this position paper, we propose an approach for Web Observatories that builds on using social media, personal data, and sensors to build Persona for an individual, but also use this data and the concept of Focused Micro Blogs (FMB) for situation detection, helping individual using situation action rules, and finally gaining insights for obtaining insights about society. We demonstrate this in a concrete use case of fitness and health care related sensors for building health persona and using this for understanding societal health issues.

The so-called real-time web (RTW) is a web of opinions, comments, and personal viewpoints, often expressed in the form of short, 140-character text messages providing abbreviated and personalized commentary in real-time. Twitter is undoubtedly the king of the RTW. It boasts 100+ million users and generates in the region of 50m tweets per day. This RTW data is far from the structured data (ratings, product features, etc.) familiar to recommender systems research, but it is useful to consider its applicability to recommendation scenarios. In this short paper we describe an experiment to look at harnessing the real-time opinions of movie fans, expressed through the Twitter-like short textual reviews available on the Blippr service (www.blippr.com). In particular we describe how users and movies can be represented from the terms used in their associated reviews and describe a number of experiments to highlight the recommendation potential of this RTW data-source and approach.

Blog search has not yet reached its full potential. In this position paper, we suggest that more could be done to accommodate the task of finding good blogs to read, especially with respect to matching a desired taste or style of writing. We propose a faceted navigation interface as a good starting point for blog and author search, and that search oriented around people and their writings will lend itself well to advanced interfaces. We also argue that blog search is probably best integrated with search on other forms of timely social media for the task of determining what is currently being thought about a particular topic.

In this paper, we study popular microcelebrities from the Global South to understand their strategies and practices on Facebook. Unlike traditional celebrities who gain their reputation through different types of physical performance, these microcelebrities attain their status by presenting themselves in a favorable way to their online followers. We conducted interviews with 19 microcelebrities from Bangladesh and analyzed our data using actor-network theory (ANT) and Goffman’s dramaturgical analysis (DA) of human interaction. We discuss the complex socio-technical ecosystem of the microcelebrity and the roles of non-human actors, such as platforms and local internet infrastructure along with human actors’ practices. We explain the microcelebrities’ experience with the process of microcelebritification–the process of being a microcelebrity on social media through impression management, and becoming opinion leaders in local sociopolitical discourses as part of their online identity. Our paper contributes to the emerging literature on microcelebrities by highlighting the process viewed in the context of the Global South. peerReviewed

Computing technologies have already established their place in various areas of public transport control in smart cities. While the analysis of signals coming from various sensors is executed at a very high level of sophistication, information expressed by humans in natural language is still not being used in a way that takes advantage of its full potential. Existing research on text mining in public transport monitoring is focused mainly on event detection. In this paper, we present a novel approach to vehicle delay prediction based on text data. The proposed method fuses information coming from standard sources (sensors) with text messages, to construct a regression model, that predicts delays for previously unseen messages describing road conditions. The method has been implemented based on an existing public transport monitoring system in Warsaw, Poland. In the paper, we discuss it briefly. Delay prediction based on information expressed in natural language will not replace standard methods for delay prediction that involve the use of vehicle sensors. However, it offers an attractive alternative to mine for knowledge from sources such as social media.

Affective social multimedia computing is an emergent research topic for both affective computing and multimedia research communities. Social multimedia is fundamentally changing how we communicate, interact, and collaborate with other people in our daily lives. Comparing with well-organized broadcast news and professionally made videos such as commercials, TV shows, and movies, social multimedia media computing imposes great challenges to research communities. Social multimedia contains much affective information. Effective extraction of affective information from social multimedia can greatly help social multimedia computing (e.g., processing, index, retrieval, and understanding). Although much progress have been made in traditional multimedia research on multimedia content analysis, indexing, and retrieval based on subjective concepts such as emotion, aesthetics, and preference, affective social multimedia computing is a new research area. The affective social multimedia computing aims to proceed affective information from social multi-media. For massive and heterogeneous social media data, the research requires multidisciplinary understanding of content and perceptual cues from social multimedia. This workshop served as a successful step towards this goal and attracted contributions from different research disciplines on the analysis of affective signals in interaction (multimodal analyses enabling artificial agents in Human-Machine Interaction, social Interaction with artificial agents) and social multimedia (e.g., twitter, wechat, weibo, youtube, facebook, etc). This paper provides a summary of the activities of the workshop and the accepted papers and abstracts.

A cyber cyber digital twin is a deployed software model that executes in tandem with the system it simulates, contributing to, and drawing from, the system's behaviour. This paper outlines Facebook's cyber cyber digital twin, dubbed WW, a twin of Facebook's WWW platform, built using web-enabled simulation. The paper focuses on the current research challenges and opportunities in the area of measurement. Measurement challenges lie at the heart of modern simulation. They directly impact how we use simulation outcomes for automated online and semi-automated offline decision making. Measurements also encompas how we verify and validate those outcomes. Modern simulation systems are increasingly becoming more like cyber cyber digital twins, effectively moving from manual to automated decision making, hence, these measurement challenges acquire ever greater significance.

People's interactions, communications, and engagement have all changed as a result of the rise of social media. These networks are vital for expanding scope and impact. People in these networks influence each other. Social influencers spread the knowledge in the network. Identification of such influencers is a challenging task. Generally, in past studies varied qualitative metrics like centrality, connectivity etc has been popularly used for identifying the influencers. In a network it has been noticed that every person interacts in the network in context to its own interest areas. He influences specific to his interest domains. Based on this belief, the aim of this paper is to detect topic-wise influencers in social media so that we can target person or influencer appropriately for influence maximisation. This paper focuses on measuring the strength of topic-level social influence using sentiments of the text used for interaction by social network user and later fuzzy modeling has been applied. Fuzzy modeling help in finding the person probability index of influence (positive or negative) in context to a different topics he is contributing in social media. In addition, three user features- engagement Index, activity index and connectivity Index has been utilized to compute the user overall influencer score. For experimentation, the tweets from the Twitter have been used to evaluate the proposed method.

In 2018 Facebook blocked a public Application Programming Interfaces (API) that could be used to download data from Facebook and Instagram. Much uncertainty still exists about the effect on social media research due to changes in Instagram API conditions. The presented paper provides an overview of the Instagram domain in terms of a research area. The main focus of this research is on the comparison of the key topics before and after the change of the Instagram API terms (comparing Instagram's research domain before and after 2018). A partial goal was to find out how the change in the conditions of the Instagram API has changed the number of social media research itself. We used a bibliometric approach to map the domain of Instagram. The paper has identified key topics in the domain of Instagram. Between the years 2010 and 2018 the key topics were gender, behavior on social media, dissemination of information, and platform selection. After the change of Instagram API conditions, after 2018, the key topics were gratifications, body image, dissatisfaction, and basic Instagram topics. The paper has found that generally, there was no change in research topics, nor the number of papers published after the Instagram API condition. Further study should focus on establish the relationships between Instagram use and psychological well-being; investigate the motives for Instagram use a study the effect of Instagram API on research with the use of different methods; gaining a better understanding of social media consumer activity; establish whatever our key topics are relevant to other social media platforms (Facebook, Twitter or Tiktok); study Instagram domain on different citation databases (e.g., in Scopus). This paper has also raised important questions about whether the Instagram API should be or should not be open for research purposes.

Video streaming platforms such as Youtube, Twitch, and DLive allow users to live-stream video content for viewers who can optionally express their appreciation through monetary donations. DLive is one of the smaller and lesser-known streaming platforms, and historically has had fewer content moderation practices. It has thus become a popular place for violent extremists and other clandestine groups to earn money and propagandize. What is the financial structure of the DLive streaming ecosystem and how much money is changing hands? In the past it has been difficult to understand how far-right extremists fundraise via podcasts and video streams because of the secretive nature of the activity and because of the difficulty of getting data from social media platforms. This paper describes a novel experiment to collect and analyze data from DLive's publicly available ledgers of transactions in order to understand the financial structure of the clandestine, extreme far-right video streaming community. The main findings of this paper are, first, that the majority of donors are using micropayments in varying frequencies, but a small handful of donors spend large amounts of money to finance their favorite streamers. Next, the timing of donations to high-profile far-right streamers follows a fairly predictable pattern that is closely tied to a broadcast schedule. Finally, the far-right video streaming financial landscape is divided into separate cliques which exhibit very little crossover in terms of sizable donations. This work will be important to technology companies, policymakers, and researchers who are trying to understand how niche social media services, including video platforms, are being exploited by extremists to propagandize and fundraise., accepted at WebSci'21, 10 pages

Threat moderation on social media has been subject to much public debate and criticism, especially for its broadly permissive approach. In this paper, we focus on Twitter's Violent Threats policy, highlighting its shortcomings by comparing it to linguistic and legal threat assessment frameworks. Specifically, we foreground the importance of accounting for the lived experiences of harassment—how people perceive and react to a tweet—a measure largely disregarded by Twitter's Violent Threats policy but a core part of linguistic and legal threat assessment frameworks. To illustrate this, we examine three tweets by drawing upon these frameworks. These tweets showcase the racist, sexist, and abusive language used in threats towards those who have been marginalized. Through our analysis, we highlight how content moderation policies, despite their stated goal of promoting free speech, in effect, work to inhibit it by fostering an online toxic environment that precipitates self-censorship in fear of violence and retaliation. In doing so, we make a case for technology designers and policy makers working in the sphere of content moderation to craft approaches that incorporate the various nuanced dimensions of threat assessment toward a more inclusive and open environment for online discourse. CONTENT WARNING: This paper contains strong and violent language. Please use discretion when reading, printing, or recommending this paper.

This paper joins the discussion on participation in Participatory Design (PD) by proposing reflexivity as an orientation for engaging in design and for the plurality of participation. Based on our study in which a social media-supported collaboration model was designed with professionals working against workplace harassment, the paper recounts how the designer--researcher's reflexive orientation allowed participation to be uncovered as manifold and dynamic and how this affected the design process. We discuss the critical sensitivity and attentiveness enacted in design practice as reflexive engagement that encouraged negotiations of roles, goals, and technology. Regarding participation, we discuss reflexive engagement with multiple relationships, participatory assemblies and their intersections and overlaps---participation in plural---in our study. We conclude by proposing the conceptualization of reflexive engagement as an orientation for PD designers to engage in the particularities of situated design practices, while holding to the ethical (political), practical and creative commitments of PD.

Due to the rapid propagation of rumours in Online Social Networks (OSNs), identifying and understanding characteristic patterns, evolution and user behaviours behind this activity is essential. Yet, there are few tools that can support analysing rumours and activities of players in their propagation. In this paper we propose a visual analysis approach to explore rumour components and life cycles as well as their association with user actions. The approach is realized by the implementation of a prototype system, called RumourFlow. Our framework designs, adopts and implements multiple visualizations and modeling tools that are integrated to reveal rumour contents and participants' activity, both within a rumour and across different rumours. The approach supports analysts in drawing hypotheses regarding rumour propagation. This paper presents the various models, algorithms and visualizations employed for rumour strength, rumour contents, user participation, as well as the core role of the visualizations in studying this phenomenon. The effectiveness of the approach is illustrated by a use case highlighting relevant insight into rumour spreading, most of which could not otherwise be observed.

In the past, there are many researches on “social media” or “big data”, but few of them are combined. However, with the advent of the information age, the explosive growth of data makes “social media” pay attention to the field of “big data”. Since then, the research area of “social media” and “big data” (SM-BD) has gradually become the focus of research. In order to have a comprehensive understanding of the rise and development trend of the field, this paper uses bibliometric method to achieve the above purposes. By downloading 1965 articles from Web of Science, CiteSpace is used to analyze the retrieval results, mainly including: the distribution of published times, co-citation, cooperation network, burst detection, detailed analysis of the visualization results involved and relevant conclusions display. This paper makes a comprehensive survey of the development of the SM-BD research since its emergence, summarizes the research contributions of all sides, and extracts the research hotspots and new trends in this field. It provides a reference for beginners in this field and provides some ideas for relevant researchers.

In 2016, United Kingdom (UK) citizens voted to leave the European Union (EU), which was officially implemented in 2020. During this period, UK residents experienced a great deal of uncertainty around the UK's continued relationship with the EU. Many people have used social media platforms to express their emotions about this critical event. Sentiment analysis has been recently considered as an important tool for detecting mental well-being in Twitter contents. However, detecting the psychological distress status in political-related tweets is a challenging task due to the lack of explicit sentences describing the depressive or anxiety status. To address this problem, this paper leverages a transfer learning approach for sentiment analysis to measure the non-clinical psychological distress status in Brexit tweets. The framework transfers the knowledge learnt from self-reported psychological distress tweets (source domain) to detect the distress status in Brexit tweets (target domain). The framework applies a domain adaptation technique to decrease the impact of negative transfer between source and target domains. The paper also introduces a Brexit distress index that can be used to detect levels of psychological distress of individuals in Brexit tweets. We design an experiment that includes data from both domains. The proposed model is able to detect the non-clinical psychological distress status in Brexit tweets with an accuracy of 66% and 62% on the source and target domains, respectively., Comment: SAC 2021, MLA - Machine Learning and its Applications

Disseminated mainly through social media, online false information has triggered alarming negative impact on all aspects of life including politics, science, and education. In Brazil, WhatsApp has been pointed out as one of the main vehicles for disseminating false information, reaching millions of people all across the country. This paper presents an evaluation on WhatsApp with a values-oriented approach to investigate what aspects of this platform contributed to the false information spread in Brazil. The evaluation was conducted with 6 WhatsApp heavy users in a 2-hour session, identifying 16 different problems. Results revealed aspects of the technical infrastructure and user interface that influence the way different stakeholders can use the tool to promote false information. Finally, the paper presents prospective paths for improvements on the platform and possible ways to deal with the identified problems.

As a mainstream online social media platform, microblog predicts the individual forwarding behavior from the perspective of a single user, which is helpful to grasp the information dissemination characteristics in microblog network from the micro level and realize further prediction and analysis of information dissemination paths and even dissemination trends. From the perspective of a single user, this paper predicts the forwarding behavior of microblog users. This paper summarizes the previous researches on the influencing factors of microblog users' forwarding willingness, considers the influence of users' interaction and users' activity on forwarding willingness, proposes a prediction method of microblog users' forwarding behavior based on interactive and active information, and verifies its feasibility. Finally, the shortcomings of this method are summarized and the prospect is put forward.

Machine learning is used in data-driven decision making and in governmental application of algorithms. Along with its many benefits, literature shows that machine learning solutions can introduce human bias to their results that they from training data. In this paper the authors study sentiment analysis algorithms to see if they show human bias, by interchanging lists of names associated with gender, race and political orientation in a synthetic neutral template sentence and then observing how the resulting sentiment values change. As a result, the authors find that names as subjects and objects in English language texts alone do not introduce human bias to the sentences' sentiment values. The findings also suggest that pseudonymization might be a better solution to the anonymization of social media postings for data protection compliance purposes than research so far suggested, owing to the general lack of sentiment distortion of the method introduced in this paper.

This paper reflects on the political aspect of game modding in Brazil through a case study of local private servers of World of Warcraft (Blizzard, 2004-) and the Brazukas mod of Pro Evolution Soccer (PES, Konami, 2001-). These regional PC modding initiatives started as a form of conditioned access to the games and the global community/market around them and became active forms of participation and intervention that shaped gaming in general in the country. Following their traces on social media, interviewing some of the key stakeholders and playing the mods, I argue that precariousness is a central aspect of both the final product and their assemblage. This paper documents these processes as a decolonial approach to technology that addresses the socioeconomical context of scarcity, but ultimately leads to a larger epistemological critique.

In this paper we introduce the concept of the “base meme” for characterizing unique information artifacts that are used to make derivative, new, and related memes. Base memes are antecedents to many versions of derivative memes that are published all across the web. While they can be created in meme template generator websites, their origins and diffusion can be difficult for researchers to verify. Despite the often ephemeral nature of memes that are shared via platforms, they can be fairly reliably found in web archive collections, such as the Internet Archive and the US Library of Congress’ Web Cultures Web Archive. In this paper, we first present the existing research on memes and discuss the challenges for researchers who study them (such as identification and language detection). We then describe the importance of web archives to social media research and building robust methods of inquiry for internet history. Using archived data from the Library of Congress’ Meme Generator Archive (N=57,652), we use descriptive analysis to calculate, measure, and describe this important public web archive of memes. Our results show that this collection has a variety of “base memes” that can be grouped with their related derivative memes (which we consider to be their related works). We use language detection software to identify a variety of languages present in the archived dataset of memes. We close by describing why approaching these metrics on “base meme” image macros alongside findings for derivative versions and the multiple languages present in web archives of social media allows researchers to study a diversity of voices, including linguistic diversity, distinctions in humor, and the variety of cultural expressions present in memes.

Social media is a popular source for political communication and user engagement around social and political issues. While the diversity of the population participating in social and political events in person are often considered for social science research, measuring the diversity representation within online communities is not a common part of social media analysis. This paper attempts to fill that gap and presents a methodology for labeling and analyzing diversity in a social media sample based on emojis and keywords associated with gender, skin tone, sexual orientation, religion, and political ideology. We analyze the trends of diversity related themes and the diversity of users engaging in the online political community during the leadup to the 2018 U.S. midterm elections. Our results reveal patterns along diversity themes that otherwise would have been lost in the volume of content. Further, the diversity composition of our sample of online users rallying around political campaigns was similar to those measured in exit polls on election day. The diversity language model and methodology for diversity analysis presented in this paper can be adapted to other languages and applied to other research domains to provide social media researchers a valuable lens to identify the diversity of voices and topics of interest for the less-represented populations participating in an online social community.

Social media plays an increasingly important role in people's lives, transforming people from passive recipients to active creators of information and influencing all aspects of society in a unique way. Behavioral finance studies have shown that stock investors are prone to making decisions that are not in line with expectations due to external emotions. This paper studies the impact of the Sade incident on the economy of China and South Korea, and studies and predicts the change of the overall stock prices in China and Korea through the microblog emotions related to Sade events. This paper uses text mining technologies to analyze the text of the related microblog of Sade incident from July 2016 to May 2017 in Sina Weibo. The time series of emotional evaluation index and emotional tendency of blog posts are generated. Based on VAR model and Granger analysis, the correlation between the SSE/KOSPI index and the emotional tendency is tested, and further the support vector machine model, optimized by the particle swarm optimization, is established to predict the price change of the stock market in China and South Korea, and to analyze the influence of the Sade event on China and Korea. The experimental results show that the SVM model achieves higher prediction accuracy after introducing the emotional feature. The accuracy of the forecast in South Korea is especially significant, indicating that the impact of the Sade event on the Korean stock market is more severe than that in China.

This paper identifies more opportunities for Politicians to present themselves as a better person and to make positive relationships with citizens through social media channels. Social media is a new dimension that can be studied to look at the use of social media for creating better relationships between Politicians and citizens. As the social media is developing rapidly day by day, this study is an exploratory step toward the ways a Politician uses social media channels. The main purpose of this paper is to draw attention on politicians/citizens relationship through social media. It shows how politicians create websites to influence citizens mind and to make positive image by giving all the information about their present and future plans, how they use these pages for their betterment, and what type of results are generated in the form of citizens satisfaction and their loyalty towards politicians. For this study, a quantitative study approach is used. Data is collected from different people who use social networking sites. Convenience sampling technique is adopted to collect data from government servants and university students in two cities of Pakistan. The main purposes or actions which were done by the politicians are making positive image through interactions with citizens, giving answers to the citizens, providing useful information to citizens and handling their negative responses. It depicts what type of relationship is cultivated by using social networking sites, how people perceive that information and what will be the reactions of the citizens about this type of communication, which are made on social media by the politicians. Interaction with citizens, boosting/improving relationship with citizens, creating awareness about Politician image, citizen's involvement and promoting positive actions were measured in this study by understanding citizen's mind through the data collection through questionnaires.

In this paper, we characterize the content and discourse on BitChute, a social video-hosting platform. Launched in 2017 as an alternative to YouTube, BitChute joins an ecosystem of alternative, low content moderation platforms, including Gab, Voat, Minds, and 4chan. Uniquely, BitChute is the first of these alternative platforms to focus on video content and is growing in popularity. Our analysis reveals several key characteristics of the platform. We find that only a handful of channels receive any engagement, and almost all of those channels contain conspiracies or hate speech. This high rate of hate speech on the platform as a whole, much of which is anti-Semitic, is particularly concerning. Our results suggest that BitChute has a higher rate of hate speech than Gab but less than 4chan. Lastly, we find that while some BitChute content producers have been banned from other platforms, many maintain profiles on mainstream social media platforms, particularly YouTube. This paper contributes a first look at the content and discourse on BitChute and provides a building block for future research on low content moderation platforms.

Digital divide remains a major concern among indigenous people despite the rapid increase in mobile phone penetration. Beyond the appalling participatory gaps, the devaluing of humanity in the digital era is seen as a much bigger threat. This paper addresses the emerging challenges that are putting indigenous communities at a great disadvantage. The use of emoji in social media and its prominence as a visual language for delivering emotional expressions, gestures and action in a speedy manner is fast becoming a way of life. The universality of emoji poses a major problem to the sustainability of the indigenous cultural values as it imposes western cultural and social behaviour hegemony on the indigenous society. Interactions with remote rural communities in Borneo over the past twenty years have revealed the devastating effect that such an outside-in communication medium has on their cultural resilience. Diversifying the emoji design in adopting a local cultural flavour alone, as described in current literature, will not address the issue effectively. Without considering the whole socio-technical system at a macro-level, the dangers relating to the aggravated disconnect to their rich traditional way of life cannot be averted. We posit a holistic socio-technical systems innovation approach with the participatory involvement of the indigenous community. In this paper, we demonstrate the modeling of complex systems that embed the socio-cultural context without compromising cultural values.

The African Sahel has become a stage for increasing violent conflict since 2009. It is probably no co-incidence that the upsurge of these conflicts went together with the increase in digital connectivity in the region. In this paper I explore the relationship between radicalization, conflict and access to ICTs and social media in the Sahel. First I sketch how the social landscape has changed due to the advancement of the ‘digital highway’. We will see how the so-called hybrid organization, the democratization forces of the digital have influenced the formation of trans-national communities and through these influenced conflict dynamics. The second part of the paper explores the increase in violence and the potential role of the digital networks in the upsurge of this violence. Radicalization in different forms (ethnic, religious, national) is an important effect of the information flows that travel the digital networks and that seems to influence the increasing (ugly) violence.

To build a human-centric Web, we need a solid understanding of human connections online and of mechanisms for fostering such connections. People both perceive and respond to digital technologies and form connections with one another in diverse ways. Whether the goal is to improve engagement or to foster community, modelling user groups to personalise or tailor experiences can be key. Tailoring and connection-building are particularly crucial in spaces such as healthcare and education, where evidence clearly shows that perceived social support can facilitate learning and enhance outcomes. PC’20 is a half-day workshop including two invited speakers, two discussion papers, and a broader discussion session. The first keynote is from Su White, an Associate Professor at the University of Southampton. She will speak about the facilitation of education online. The second keynote is from Michael Fergusson, CEO of Ayogo Health Inc. He will speak about “The Architecture of Choice: Using Psychosocial Variables to Dynamically Tailor Interventions.” A session will highlight two discussion papers. Firstly, Jennifer Golbeck from the University of Maryland will present her work “Improving Emotional Well-Being on Social Media with Collaborative Filtering”. Secondly, Roushdat Elaheebocus, Poovanen Seenan, Sheekah Beharry and Girishsing Caussyram from the University of Mauritius will present their work “BehaviourCoach: A Customisable and Socially-Enhanced Exergaming Application Development Framework”. Finally, a broader discussion session will consider: tools, techniques and case studies in tailoring and community-building; how these relate to one another; and next steps for Web Science researchers. The workshop builds on health and education communities established through previous Web Science conference workshops. By using these two domains to ground discussion of user modelling and community, we intend to reinvigorate these communities. A summary of the workshop will be created and shared online within two weeks of the event. We thank the members of our program committee: Stephane Bazan (TomKeen) and Charlie Hargood (University of Bournemouth).

Online hate speech is an important issue that breaks the cohesiveness of online social communities and even raises public safety concerns in our societies. Motivated by this rising issue, researchers have developed many traditional machine learning and deep learning methods to detect hate speech in online social platforms automatically. However, most of these methods have only considered single type textual feature, e.g., term frequency, or using word embeddings. Such approaches neglect the other rich textual information that could be utilized to improve hate speech detection. In this paper, we propose DeepHate, a novel deep learning model that combines multi-faceted text representations such as word embeddings, sentiments, and topical information, to detect hate speech in online social platforms. We conduct extensive experiments and evaluate DeepHate on three large publicly available real-world datasets. Our experiment results show that DeepHate outperforms the state-of-the-art baselines on the hate speech detection task. We also perform case studies to provide insights into the salient features that best aid in detecting hate speech in online social platforms., Comment: Paper Accepted for 12th International ACM Conference on Web Science

Humans, as social beings, regularly create social ties with each other. Hence, social networks of interconnected humans - such as friendship or collaboration networks - are commonplace. In this context, the proliferation of social media such as Facebook, Reddit, and Twitter have significantly influenced how humans form social ties, resulting in an abundance of social-media-based social networks, or more briefly, social media networks [3].In line with this development, a growing number of organizations are using enterprise social media (ESM) to support their internal communication and collaboration [2]. Analogous to public social media, the collective use of ESM creates ESM networks that connect employees across departments and hierarchical levels [4]. Consistent with this scenario, considerable research has examined the structures of ESM networks. However, few researchers have addressed the evolution of ESM networks - that is, how ESM network structures evolve over time [5].Disregarding such evolution has two main shortcomings [1]. First, it impedes studying how ESM network structures such as dyads emerge, are sustained, and dissolve over time. Second, it hampers the analysis of how external factors such as changes in ESM platforms or organizational measures affect ESM network structures. Addressing these shortcomings would enable insights to better manage ESM networks. For instance, these insights could be used to support employee socialization processes such as onboarding processes in ESM networks, or to adjust ESM platforms to optimize their associated ESM networks.Within this context, the current paper proposes a research project that would examine the evolution of ESM network structures by answering three research questions. First, what are the underlying processes of ESM network evolution from employee level to global network level? Second, what are the antecedents of these evolutionary processes? Third, what are the implications of these evolutionary processes for employees, groups, and organizations? To address these questions, the proposed research project will entail an initial literature review. The review will enable a summary of the status of research on ESM network evolution and the identification of knowledge gaps. The most relevant gaps will then be addressed through empirical studies to analyze specific processes of ESM network evolution. Each empirical study will apply a data-driven approach to reconstruct the observed longitudinal ESM networks using the databases of their underlying ESM platforms. This data thus obtained will be precise and can be studied via stochastic models for empirical social network analyses. Such analysis would address the statistical interdependencies in ESM networks.The research project proposed in this paper is aimed at contributing to a comprehensive understanding of ESM network evolution in general. The results could be used in further research, for instance, to develop ESM network evolution models [1]. Moreover, the findings may enable organizations to optimize their ESM networks [5].

In as much as the approaches of the new revolution, machines including transmission media like social media sites, nowadays quantity of data swell hastily. So, size is the core and only facet that leaps the mention of BIG DATA. In this article, an effort to touch a comprehensive view of big data technologies, because of the swift evolution of data by an industry trying the academic press to catch up. This paper also offers a unified explanation of big data as well as the analytics methods. A practical discriminate characteristic of this paper is core analytics associated with unstructured data which is more than 90% of big data. To deal with complicated Big Data problems, great work has been done. This paper analyzes contemporary Big Data technologies. Therein article further strengthens the necessity to formulate new tools for analytics. It bestows not sole an intercontinental overview of big data techniques even though the valuation according to big data Hadoop Ecosystem. It classifies and debates the main technologies feature, challenges, and usage as well.

This paper joins the growing body of work in postcolonial computing in HCI, and critically examines the impacts of online social media on the urban architecture in the Global South. Based on our nine-month long ethnography at eight residential areas in Dhaka, Bangladesh, this paper reports how online fame drives local users to produce digital images of their houses mimicking various Western standards, which in turn, brings changes to the organization, aesthetics, and functions of domestic spaces. This paper also describes how such digital image mediated transformations to local architecture are diminishing traditional spaces, altering their usual functions, and limiting the movement of many women inside their home. Drawing from a rich body of literature in postcolonialism, critical image theory, architecture, and Islamic feminism, we explain how these practices demonstrate a subaltern experience of using social media in the Global South. We further discuss design implications to both HCI and architecture to address these issues, and connect our findings to the broader agendas of HCI around social justice and global development.

Citing, quoting, and forwarding & commenting behaviors are widely seen in academia, news media, and social media. Existing behavior modeling approaches focused on mining content and describing preferences of authors, speakers, and users. However, behavioral intention plays an important role in generating content on the platforms. In this work, we propose to identify the referential intention which motivates the action of using the referred (e.g., cited, quoted, and retweeted) source and content to support their claims. We adopt a theory in sociology to develop a schema of four types of intentions. The challenge lies in the heterogeneity of observed contextual information surrounding the referential behavior, such as referred content (e.g., a cited paper), local context (e.g., the sentence citing the paper), neighboring context (e.g., the former and latter sentences), and network context (e.g., the academic network of authors, affiliations, and keywords). We propose a new neural framework with Interactive Hierarchical Attention (IHA) to identify the intention of referential behavior by properly aggregating the heterogeneous contexts. Experiments demonstrate that the proposed method can effectively identify the type of intention of citing behaviors (on academic data) and retweeting behaviors (on Twitter). And learning the heterogeneous contexts collectively can improve the performance. This work opens a door for understanding content generation from a fundamental perspective of behavior sciences.

In this paper, we extend the task of semantic textual similarity to include sentences which contain emojis. Emojis are ubiquitous on social media today, but are often removed in the pre-processing stage of curating datasets for NLP tasks. In this paper, we qualitatively ascertain the amount of semantic information lost by discounting emojis, as well as show a mechanism of accounting for emojis in a semantic task. We create a sentence similarity dataset of 4000 pairs of tweets with emojis, which have been annotated for relatedness. The corpus contains tweets curated based on common topic as well as by replacement of emojis. The latter was done to analyze the difference in semantics associated with different emojis. We aim to provide an understanding of the information lost by removing emojis by providing a qualitative analysis of the dataset. We also aim to present a method of using both emojis and words for downstream NLP tasks beyond sentiment analysis.

Concurrent with the growth and widespread use of social networking platforms has been a rise in the prevalence of cyberbullying and cyberharassment, particularly among youth. Although cyberbullying is frequently defined as hostile communication or interactions that occur repetitively via electronic media, little is known about the temporal aspects of cyberbullying on social media, such as how the number, frequency, and timing of posts may vary systematically between cyberbullying and non-cyberbullying social media sessions. In this paper, we aim to contribute to the understanding of temporal properties of cyberbullying through the analysis of Instagram data. That is, the paper presents key temporal characteristics of cyberbullying and trends obtained from descriptive and burst analysis tasks. Our results have the potential to inform the development of more effective cyberbullying detection models.