Introduction to the 2019 HICSS Special Issue
New Emoji Requests from Twitter Users: When, Where, Why, and What We Can Do About Them
Research suggests that public fear and anger in wake of a terror attack can each uniquely contribute to policy attitudes and risk-avoidance behaviors. Given the importance of these negative-valanced emotions, there is value in studying how terror events can incite fear and anger at various times and locations relative to an attack. We analyze 36,496 Twitter posts, including re-Tweets, authored in response to the 2016 Orlando nightclub shooting and examined how fear- and anger-related language varied with time and distance from the attack. Fear-related words sharply decreased over time, though the trend was strongest at locations near the attack, while anger-related words slightly decreased over time and increased with distance from Orlando. Comparing these results to users? pre-attack emotional language suggested that distant users remained both angry and fearful after the shooting, while users close to the attack remained angry but quickly reduced expressions of fear to pre-attack levels.
Studying cultural variation in recollections of sociopolitical events is crucial for achieving diverse understandings of such events. To date, most studies in this area have focused on analyzing variation in texts describing events. Here we analyze variation in image usage across Wikipedia language editions to understand if like text, visual narratives reflect distinct perspectives in articles about culturally-tethered events. We focus on articles about coup d?états as an example of highly contextual sociopolitical events likely to display such variation.The key challenge to examining variation in images is that there is no existing framework to use as a basis for comparison. To address this challenge, we use an iterative inductive coding process to arrive at a 46-item typology for categorizing the content of images relating to contested sociopolitical events, and a typology of network motifs that characterizes structural patterns of image use. We apply these typologies in a large-scale quantitative analysis that establishes clusters of image themes, two detailed qualitative case studies comparing Wikipedia articles on coup d?états in Soviet Russia and Egypt, and four quantitative analyses clustering image themes by language usage at the article level. These analyses document variation in imagery around particular events and variation in tendencies across cultures. We find substantial cultural variation in both content and network structure. This study presents a novel methodological framework for uncovering culturally divergent perspective of political crises through imagery on Wikipedia.
In the landscape of online social networking sites, many platforms are reaching a scale and longevity that require designers to address the post-mortem data interactions that follow people's deaths. To evaluate the experiences and challenges people face when caring for memorialized profiles, we conducted 28 qualitative interviews with people serving as legacy contacts for memorialized Facebook accounts. We report on who legacy contacts are, their practices and their expectations, and find that people were chosen to be a legacy contact for one major reason: trust. In our analysis we find disconnects between how people understand trust in the context of interpersonal relationships and how trust is technically implemented. We conclude by discussing the persistent challenges of representing the ambiguity of interpersonal trust in impersonal, computational systems.
In recent years, streaming platforms for video games have seen increasingly large interest, as so-called "esports" have developed into a lucrative branch of business. Like for other sports, watching esports has become a new kind of entertainment medium, which is possible due to platforms that allow gamers to live stream their gameplay, the most popular platform being Twitch.tv. On these platforms, users can comment on streams in real-time and thereby express their opinion about the events in the stream. Due to the popularity of Twitch.tv, this can be a valuable source of feedback for streamers aiming to improve their reception in a gaming-oriented audience. In this work, we explore the possibility of deriving feedback for video streams on Twitch.tv by analyzing the sentiment of live text comments made by stream viewers in highly active channels. Automatic sentiment analysis on these comments is a challenging task, as one can compare the language used in Twitch.tv with that used by an audience in a stadium, shouting as loud as possible in sometimes non-organized ways. This language is very different from common English, mixing Internet slang and gaming-related language with abbreviations, intentional and unintentional grammatical and orthographic mistakes as well as emoji-like images called emotes. Classic lexicon based sentiment analysis techniques therefore fail when applied to Twitch comments. In order to overcome the challenge posed by the non-standard language, we propose two unsupervised lexicon based approaches that make heavy use of the information encoded in emotes, as well as a weakly supervised neural network based classifier trained on the lexicon based outputs, that is supposed to help generalization to unknown words by use of domain-specific word embeddings. To enable better understanding of Twitch.tv comments, we analyze a large dataset of comments, uncovering specific properties of their language and provide a smaller set of comments labeled with sentiment information by crowd sourcing. We present two case studies showing the effectiveness of our methods in generating sentiment trajectories for events live-streamed on Twitch.tv that correlate well with specific topics in the given stream. This allows for a new kind of implicit real-time feedback gathering for Twitch streamers and companies producing games or streaming content on Twitch. We make our datasets as well as our code publicly available for further research.
As emojis are increasingly used in everyday online communication such as messaging, email, and social networks, various techniques have attempted to improve the user experience in communicating emotions and information through emojis. Emoji recommendation is one such example in which machine learning is applied to predict which emojis the user is about to select, based on the user?s current input message. While emoji suggestion helps users identify and select the right emoji among plethora of emojis, analyzing only a single sentence for this purpose has several limitations. First, various emotions, information, and contexts that emerge in a flow of conversation could be missed by simply looking at the most recent sentence. Second, it cannot suggest emojis for emoji-only messages where the users only use emojis without any text. To overcome these issues, we present Reeboc (Recommending emojis based on context) that combines machine learning and k-means clustering to analyze the conversation of a chat, extract different emotions or topics of the conversation, and recommend emojis that represent various contexts to the user. To evaluate the effectiveness of our proposed emoji recommendation system and understand its effects on the user experience, we performed a user study with 17 participants in 8 groups in a realistic mobile chat environment with three different modes: (i) a default static layout without emoji recommendations, (ii) emoji recommendation based on the current single sentence, and (iii) our emoji recommendation model that considers the conversation. Participants spent the least amount of time in identifying and selecting the emojis of their choice with Reeboc (38% faster than the baseline). Also, they chose emojis that were more highly ranked with Reeboc than with current-sentence only recommendations. Moreover, participants appreciated emoji recommendations for emoji-only messages, which consisted of 36.2% of all sentences containing emojis.
Recently, there is a strong interest in measuring influence in online social networks. Different measures have been proposed to predict when individuals will adopt a new behavior, given the influence produced by their friends. In this paper, we show one can achieve significant improvement over these measures, extending them to consider a pair of time constraints that provide a better proxy for social influence. By conducting an engineering study that investigates retweet networks from Twitter and Sina Weibo datasets, we tune those two parameters while we examine the correlation between influence and the probability of adoption as well as the ability to predict adoption, estimating the real susceptibility and influence that microblog users are dynamically subjected to. Although there are limitations about using retweets to analyze social influence, our results show that for the simple count of active neighbors, its correlation with the probability of adoption is boosted up to 518.75%, while similar gains are observed for the other influence measures analyzed. We also obtain up to 18.89% improvement of F1 score when comparing to recent machine learning techniques that aim to predict adoption, enabling practical use of the corresponding concepts for social influence applications.
An online survey, the Understanding Emoji Survey, was conducted to investigate how English-speaking social media users interpret the pragmatic functions of emoji in examples adapted from public Facebook comments, based on a modified version of ?s taxonomy of functions. Of the responses received (N=519; 351 females, 120 males, 48 ?other?; 354 under 30, 165 over 30, age range 18-70+), tone modification was the preferred interpretation overall, followed by virtual action, although interpretations varied significantly by emoji type. Female and male interpretations were generally similar, while ?other? gender respondents differed significantly in dispreferring tone and preferring multiple functions. Respondents over 30 often did not understand the emoji functions or interpreted them literally, while younger users interpreted them in more conventionalized ways. Older males were most likely, and younger females were least likely, to not understand emoji functions and to find emoji confusing or annoying, consistent with previously reported gender and age differences in attitudes toward and frequency of emoji use.
Current theories struggle to explain how participants in peer-production self-organize to produce high-quality knowledge in the absence of formal coordination mechanisms. The literature traditionally holds that norms, policies, and roles make coordination possible. However, peer-production is largely free from workflow constraints and most peer-production communities do not allocate or assign tasks. Yet, scholars have suggested that ordered work sequences can emerge in such settings. We refer to sequences of activities that emerge organically as components of ?emergent routines?. The volunteer nature of peer-production, coupled with high degrees of turnover, makes learning and coordination difficult, calling into question the extent to which emergent routines could be ingrained in the community. The objective of this paper is to characterize the work sequences that organically emerge in peer-production, as well as to understand the temporal dynamics of these emergent routine components. We center our empirical investigation on the peer-production of a set of 1,000 Wikipedia articles. Using a dataset of labelled wiki work, we employ Variable-Length Markov Chains (VLMC) to identify sequences of activities exhibiting structural dependence, cluster the sequences to identify components of emergent routines, and then track their prevalence over time. We find that work is organized according to several routine components and that the prevalence of these components changes over time.
Using fitness trackers to generate and collect quantifiable data is a widespread practice aimed at better understanding one?s health and body. The intentional design of fitness trackers as genderless or universal is predicated on masculinist design values and assumptions that do not result in "neutral" devices and systems. Instead, ignoring gender in the design of fitness tracking devices marks a dangerous ongoing inattention to the needs, desires, and experiences of women, as well as transgender and gender non-conforming persons. We utilize duoethnography, a methodology emphasizing personal narrative and dialogue, as a tool that promotes feminist reflexivity in the design and study of fitness tracking technologies. Using the Jawbone UP3 as our object of study, we present findings that illustrate the gendered physical and interface design features and discuss how these features reproduce narrow understandings of gender, health, and lived experiences.
We report on two studies undertaken to establish the factors that affect funders? trust and likelihood to invest in crowdfunding campaigns online. Findings from an initial small-scale qualitative study are reported and subsequently triangulated in a larger quantitative survey. Across these studies, we demonstrate the importance of social information within the peer economy, with a strong reliance on other users across the course of the investment decision-making process. Decision-making on whether to invest is informed not only by the content of the crowdfunding campaign page, but also by social influence factors that are present (e.g. liking of the campaigner, reciprocity). It is further shaped by due diligence checks focused on the progression of the prototype for which funding is sought, and by assurances from outside the campaign page ? namely on social media. Such assurances center around the importance of social proof, garnered through opinions of previous customers or other funders. The risk here is that much of this information is content that can be faked, and as such a reliance on this may leave potential funders vulnerable. Meanwhile, a genuine lack of prior experience and customer base means that some legitimate campaigners struggle to gain trust from potential investors who are reliant on such social information. Our findings present an empirical grounding to develop future security solutions that (i) protect existing funders and (ii) increase potential funders? level of trust, to encourage their engagement with legitimate crowdfunding campaigns.
Social isolation has been identified as a major risk in elderly people living alone because of their association with cognitive decline, depression and other mental health related issues. Ambient Assisted Living (AAL) is identified as a key technology to facilitate independent living and maintain social connnectedness between elderly, their families and caregivers. AAL combines Internet of Things (IoT), Smart Homes, and machine learning to produce a smart solution that encourages independent, safe and socially active life for elderly people within their own home. In this paper, we propose, develop, implement and validate a novel Internet of Things-based solution that uses passive (i.e. non-obstructive methods) sensing for real-time monitoring of elderly in their homes. The significance of the proposed solution is in the use of machine learning and statistical models to automatically build a personalised model by learning normal behavioural pattern for the person from deployed sensors in the house. It then uses this model to detect significant changes in behavioural pattern should they occur that could be a consequence of possible health deterioration. We evaluate the performance of the proposed solution via real-world in-home trials installed in six elderly people's home for a period from 1.5 to 4 months. A discussion and analysis of the in-home trial outcomes and feedback from elderly who participated in the trials conclude the paper.
Virtual teams that use integrated communication platforms are ubiquitous in cross-border collaboration. This study explored the use of communication media and team outcomes ? both social outcomes and task accomplishment ? in multilingual virtual teams. Based on surveys from 96 virtual teams (with 578 team members), the research showed that more time spent in rich communication channels, such as online conferences, increased inclusion and satisfaction, whereas more time spent with written communication that is lower in richness increased the level of task accomplishment. Team members with lower language proficiency felt less included in all collaboration channels, whereas team members with higher language proficiency felt less satisfied with lean collaboration. Also, limited language proficiency speakers were significantly less likely to view rich tools as helpful for their teams to reach a mutual decision. Our data supports media richness theory in its original context for native and highly proficient English speakers. Our study extends the scope of the theory by applying it to the new context of team members with limited language proficiency. Management should implement a collaboration infrastructure consisting of communication platforms that integrate a variety of media to account for different tasks and different communication needs.