OpenRaiser/Intern-Atlas huggingface dataset:
Problems and Opportunities of Working with a Telco's Large Data Sets of Mobile Data
Authors: Leo Ferres
Venue: WWW
Year: 2019

Since 2016, through an association between Telefónica R&D and the Institute of Data Science in Chile, a group of researchers and myself have been working with trillions of digital traces left behind when people use their mobile phones. All of this work has been done under the general umbrella term of "data science for social good", and we have worked on anything from population displacement after external events like earthquakes, how people started using public spaces after the introduction of a popular mobile game, to actual social inclusion of people of different socio-economic backgrounds mixing in shopping malls or reading certain kinds of news, or patterns arising from gendered data sets. We will show how data in the private sector made us learn important social lessons such as how parks can become more secure when people went out to play Pokemon Go, how certain malls are hubs of social inclusion, how gender segregates the city and how different demographics keep themselves in their own informational filter bubble. However, even after all this benefits, the relationship with industry has never been fluid, and involves a lot of small and not so small compromises and "battles". In this talk, I will present a technical history of the work we've done with X/CDRs for social good including practical aspects of accessing and sharing data, the balance of research and industrial innovation, and issues of transactions costs while still providing value for the company itself, government, the university and society. I will also recount experiences about what it meant for a company like Telefónica and a research university like us to travel together in a very interesting context of huge data, incredible insights, privacy considerations, money, corporate interests, university expectations, and data-driven discovery.
I'm Lonely. Who should I talk to?
Authors: Lauren Fratamico, Deb Roy
Venue: WWW
Year: 2019

Loneliness is becoming a global epidemic. As many as 33% of Americans report being chronically lonely, with similar percentages being reported in countries around the world. Additionally, this is a percentage that has risen in recent years. Many are turning to online forums as a way to connect with others about their feelings of loneliness and to begin to reduce these feelings. However, oftentimes, posts go unresponded to and online conversations do not take place, perhaps because those conversing did not find a connection with each other, potentially leaving the poster feeling even more lonely. This paper introduces a human-in-the-loop approach so that computers can mediate interactions online about loneliness and facilitate more intimate interactions. We also discuss ways to mitigate the bias when creating this system. The artificial intelligence in this approach takes into account the homophilous characteristics of the conversations that are taking place online by examining the homophily of the participants. Initial findings related to the correlation between homophily and successful conversations about loneliness on reddit are presented, and we lay the groundwork for being able to facilitate finding optimal conversation partners for those that are feeling lonely by proposing a human-in-the-loop approach.
The First International Workshop on Knowledge Graph Technology and Applications
Authors: Yuqing Gao
Venue: WWW
Year: 2019

Decentralised data solutions bring their own sets of capabilities, requirements and issues not necessarily present in centralised solutions. In order to compare the properties of different approaches or tools for management of decentralised data, it is important to have a common evaluation framework. We present a set of dimensions relevant to data management in decentralised contexts and use them to define principles extending the FAIR framework, initially developed for open research data. By characterising a range of different data solutions or approaches by how TRusted, Autonomous, Distributed and dEcentralised, in addition to how Findable, Accessible, Interoperable and Reusable, they are, we show that our FAIR TRADE framework is useful for describing and evaluating the management of decentralised data solutions, and aim to contribute to the development of best practice in a developing field.
#Default #Interactiveart #Audiencexperience
Authors: Manuela Garretón, Andrea Rihm, Denis Parra
Venue: WWW
Year: 2019

The last decades have shown us a growing interest in different fields of how interactive art transforms the position of the viewer into a participant and how audiences engage and relate to interactive artwork. This article presents a visual analysis of the content shared on Instagram by the audience of Default –an interactive art installation presented in Santiago, Chile in 2017. The analysis shows that people reacted and engaged differently with various aspects of the installation, as shown by the strategies they used to share it. We argue that the analysis of the visual content of Instagram posts opens avenues to understanding the relationship between installation and audience, giving clues on the audience experience and, therefore, providing feedback for developers, who could use them in the design process of future installations.
Extracting Meaningful Insights on City and Zone Levels Utilizing US Open Government Data
Authors: Samaa Gazzaz, Praveen Rao
Venue: WWW
Year: 2019

It is estimated that merely 4% of the world's population reside on US soil. Remarkably, 43% of the entire population of prominent websites are hosted in the United States (Fig. 1). Even though most data content on the Web is unstructured, the US government has had big contributions in producing and actively releasing structured datasets related to different fields such as health, education, safety and finance. Aforementioned datasets are referred to as Open Government Data (OGD) and are aimed at increasing the structured data pool in conjunction with promoting government transparency and accountability. In this paper, we present a new system “OGDXplor” which processes raw OGD through a well-defined procedure leveraging machine learning algorithms and produces meaningful insights. The novelty of this work is encompassed by the collective approach utilized in developing the system and tackling challenges. First by addressing arising challenges due to data being collected and aggregated from heterogeneous sources that otherwise would have been impossible to acquire as a comprehensive unit. moreover, classification and comparisons are drawn on a much finer level that we refer to as zone level. Zones are the areas encompassed and defined by zip codes and are seldomly used in classifying and extracting insights as presented here. OGDXplor facilitates comparing and classifying zones located in different cities or zones within an individual city. The system is presented to end-users as a web application allowing users to elect zones and features relevant to their use case. Results are presented in both chart and map formats which aids the decision-making process.
Emoji Sequence Use in Enacting Personal Identity
Authors: Jing Ge
Venue: WWW
Year: 2019

Focusing on a Chinese social media platform, this study adopts computer-mediated discourse analysis to examine how users employ emoji sequences to construct their personal identity through the expression of stance and engagement. Seven types of linguistic elements were identified by conducting stance and engagement analysis on emoji sequences in posts by social media influencers. Stance was more frequent than engagement. Attitude markers were the most common element used to convey stance, whereas directive was the most prevalent element used to express engagement. In addition, emoji sequences that did not convey stance and engagement were coded as n/a. This study also observed creative usages in the composition of emoji sequences that compensate for the lack of a prescribed emoji sequence grammar. Based on these findings, it advances recommendations for the design of emoji and of social media platforms grounded in linguistic principles.
Information Extraction with Humans in the Loop
Authors: Anna Lisa Gentile
Venue: WWW
Year: 2019

Information Extraction (IE) techniques enables us to distill Knowledge from the abundantly available unstructured content. Some of the basic IE methods include the automatic extraction of relevant entities from text (e.g. places, dates, people, ...), understanding relations among them, building semantic resources (dictionaries, ontologies) to inform the extraction tasks, connecting extraction results to standard classification resources. IE techniques cannot decouple from human input - at bare minimum some of the data needs to be manually annotated by a human so that automatic methods can learn patterns to recognize certain type of information. The human-in-the-loop paradigm applied to IE techniques focuses on how to better take advantage of human annotations (the recorded observations), how much interaction with the human is needed for each specific extraction task.
The few-get-richer: a surprising consequence of popularity-based rankings?
Authors: Fabrizio Germano, Vicenç Gómez, Gaël Le Mens
Venue: WWW
Year: 2019

Ranking algorithms play a crucial role in online platforms ranging from search engines to recommender systems. In this paper, we identify a surprising consequence of popularity-based rankings: the fewer the items reporting a given signal, the higher the share of the overall traffic they collectively attract. This few-get-richer effect emerges in settings where there are few distinct classes of items (e.g., left-leaning news sources versus right-leaning news sources), and items are ranked based on their popularity. We demonstrate analytically that the few-get-richer effect emerges when people tend to click on top-ranked items and have heterogeneous preferences for the classes of items. Using simulations, we analyze how the strength of the effect changes with assumptions about the setting and human behavior. We also test our predictions experimentally in an online experiment with human participants. Our findings have important implications to understand the spread of misinformation.
Responsible Data Science
Authors: Lise Getoor
Venue: WWW
Year: 2019

Data science is an emerging discipline that offers both promise and peril. Responsible data science refers to efforts that address both the technical and societal issues in emerging data- driven technologies. How can data-driven systems reason effectively about complex dependencies and uncertainty? Furthermore, how do we understand the ethical and societal issues involved in data-driven decision-making? There is a pressing need to integrate algorithmic and statistical principles, social science theories, and basic humanist concepts so that we can think critically and constructively about the socio-technical systems we are building. In this talk, I will overview this emerging area.
Extracting actionable information from Security Forums
Authors: Joobin Gharibshah, Michalis Faloutsos
Venue: WWW
Year: 2019

The goal of this work is to systematically extract information from hacker forums, whose information would be in general described as unstructured: the text of a post is not necessarily following any writing rules. By contrast, many security initiatives and commercial entities are harnessing the readily public information, but they seem to focus on structured sources of information. Here, we focus on the problem of analyzing text content in security forums. A key novelty is that we use user profiles and contextual features along with transfer learning approach and also embedding space to help us identify and refine information that we could not get from security forum with trivial analysis. We collect a wealth of data from 5 different security forums. The contribution of our work is twofold; (a) we develop a method to automatically identify through the forums malicious IP addresses (b) we also propose a systematic method to identify and classify user-specified threads of interest into four categories. We further showcase how this information can inform knowledge extraction from the forums. As the cyber-wars are becoming more intense, having early accesses to useful information becomes more imperative to remove the hackers first-move advantage, and our work is a solid step towards this direction.
An Empirical Study of Malicious Threads in Security Forums
Authors: Joobin Gharibshah, Zhabiz Gharibshah, Evangelos E. Papalexakis, Michalis Faloutsos
Venue: WWW
Year: 2019

How useful is the information that a security analyst can extract from a security forum? We focus on threads of interest, which we define as: (i) alerts of worrisome events, such as attacks, (ii) offering of malicious services and products, (iii) hacking information to perform malicious acts, and (iv) useful security-related experiences. The analysis of security forums is in its infancy despite several promising recent works. Here, we leverage our earlier work in thread analysis, and ask the question: what kind of information do these malicious threads provide. Specifically, we analyze threads in three dimensions: (a) temporal characteristics, (b) user-centric characteristics (c) content-centric properties. We study threads pulled from three security forums spanning the period 2012-2016. First, we show that 53% of the users asking/selling malicious Services on average has 3 posts and initiate 1 thread and 1 day lifetime. Second, we argue that careful analysis can help to identify emerging threats reported in security forums through Services and Alerts threads and potentially help security analysts prevent attacks. We see this study as a first attempt to argue for the wealth and type of information that can be extracted from security forums.
On Preserving Sensitive Information of Multiple Aspect Trajectories In-House
Authors: Spyros Giotakis, Nikos Pelekis
Venue: WWW
Year: 2019

Nowadays, human trajectories are enriched with semantic information having multiple aspects, such as by using background geographic information, by user-provided data via location-based social media, as well as by data coming from various kind of sensing devices. This new type of multiple aspects representation of personal movements as sequences of places visited by a person during his/her movement poses even greater privacy violation threats. This paper provides the blueprint of a semantic-aware Moving Object Database (MOD) engine for privacy-aware sharing of such enriched mobility data and introduces an attack prevention mechanism where all potential privacy breaches that may occur when answering a query, are prevented through an auditing methodology. Towards enhancing the user-friendliness of our approach, we propose a mechanism whose objective is to modify the user queries that cannot be answered due to possible privacy violation, to ‘similar’ queries that can be answered without exposing sensitive information.
"It's almost like they're trying to hide it": How User-Provided Image Descriptions Have Failed to Make Twitter Accessible
Authors: Cole Gleason, Patrick Carrington, Cameron Tyler Cassidy, Meredith Ringel Morris, Kris M. Kitani, Jeffrey P. Bigham
Venue: WWW
Year: 2019

To make images on Twitter and other social media platforms accessible to screen reader users, image descriptions (alternative text) need to be added that describe the information contained within the image. The lack of alternative text has been an enduring accessibility problem since the “alt” attribute was added in HTML 2.0 over 20 years ago, and the rise of user-generated content has only increased the number of images shared. As of 2016, Twitter provides users the ability to turn on a feature that allows descriptions to be added to images in their tweets, presumably in an effort to combat this accessibility problem. What has remained unknown is whether simply enabling users to provide alternative text has an impact on experienced accessibility. In this paper, we present a study of 1.09 million tweets with images, finding that only 0.1% of those tweets included descriptions. In a separate analysis of the timelines of 94 blind Twitter users, we found that these image tweets included descriptions more often. Even users with the feature turned on only write descriptions for about half of the images they tweet. To better understand why users provide alternative text descriptions (or not), we interviewed 20 Twitter users who have written image descriptions. Users did not remember to add alternative text, did not have time to add it, or did not know what to include when writing the descriptions. Our findings indicate that simply making it possible to provide image descriptions is not enough, and reveal future directions for automated tools that may support users in writing high-quality descriptions.
Characterizing Speed and Scale of Cryptocurrency Discussion Spread on Reddit
Authors: Maria Glenski, Emily Saldanha, Svitlana Volkova
Venue: WWW
Year: 2019

Cryptocurrencies are a novel and disruptive technology that has prompted a new approach to how currencies work in the modern economy. As such, online discussions related to cryptocurrencies often go beyond posts about the technology and underlying architecture of the various coins, to subjective speculations of price fluctuations and predictions. Furthermore, online discussions, potentially driven by foreign adversaries, criminals or hackers, can have a significant impact on our economy and national security if spread at scale. This paper is the first to qualitatively measure and contrast discussion growth about three popular cryptocurrencies with key distinctions in motivation, usage, and implementation - Bitcoin, Ethereum, and Monero on Reddit. More specifically, we measure how discussions relevant to these coins spread in online social environments - how deep and how wide they go, how long they last, how many people they reach, etc. More importantly, we compare user behavior patterns between the focused community of the official coin subreddits and the general community across Reddit as a whole. Our Reddit sample covers three years of data between 2015 and 2018 and includes a time period of a record high Bitcoin price rise.1 Our results demonstrate that while the largest discussions on Reddit are focused on Bitcoin, posts about Monero (a cryptocurrency often used by criminals for illegal transactions on the Dark Web2) start discussions that are typically longer and wider. Bitcoin posts trigger subsequent discussion more immediately but Monero posts are more likely to trigger a longer lasting discussion. We find that moderately subjective posts across all three coins trigger larger, longer, and more viral discussion cascades within both focused and general communities on Reddit. Our analysis aims to bring the awareness to online discussion spread relevant to cryptocurrencies in addition to informing models for forecasting cryptocurrency price that rely on discussions in social media.
Quantifying and Visualizing the Demand and Supply Gap from E-commerce Search Data using Topic Models
Authors: Anjan Goswami, Prasant Mohapatra, Chengxiang Zhai
Venue: WWW
Year: 2019

The demand generation and assortment planning are two critical components of running a retail business. Traditionally, retail companies use the historical sales data for modeling and optimization of assortment selection, and they use a marketing strategy for demand generation. However, today, most retail businesses have e-commerce sites with rapidly growing online sales. An e-commerce site typically has to maintain a large amount of digitized product data, and it also keeps a vast amount of historical customer interaction data that includes search, browse, click, purchase and many other different interactions. In this paper, we show how this digitized product data and the historical search logs can be used in understanding and quantifying the gap between the supply and demand side of a retail market. This gap helps in making an effective strategy for both demand generation and assortment selection. We construct topic models of the historical search queries and the digitized product data from the catalog. We use the former to model the customer demand and the later to model the supply side of the retail business. We then create a tool to visualize the topic models to understand the differences between the supply and demand side. We also quantify the supply and demand gap by defining a metric based on Kullback-Leibler (KL) divergence of topic distributions of queries and the products. The quantification helps us identifying the topics related to excess or less demand and thereby in designing effective strategies for demand generation and assortment selection. Application of this work by e-Commerce retailers can result in the development of product innovations that can be utilized to achieve economic equilibrium. We can identify the excess demand and can provide insight to the teams responsible for improving assortment and catalog quality. Similarly, we can also identify excess supply and can provide that intelligence to the teams responsible for demand generation. Tools of this nature can be developed to systematically drive efficiency in achieving better economic gains for the entire e-commerce engine. We conduct several experiments collecting data from Walmart.com to validate the effectiveness of our approach.
Link Prediction on N-ary Relational Data
Authors: Saiping Guan, Xiaolong Jin, Yuanzhuo Wang, Xueqi Cheng
Venue: WWW
Year: 2019

With the overwhelming popularity of Knowledge Graphs (KGs), researchers have poured attention to link prediction to complete KGs for a long time. However, they mainly focus on promoting the performance on binary relational data, where facts are usually represented as triples in the form of (head entity, relation, tail entity). In practice, n-ary relational facts are also ubiquitous. When encountering such facts, existing studies usually decompose them into triples by introducing a multitude of auxiliary virtual entities and additional triples. These conversions result in the complexity of carrying out link prediction concerning more than two arities. It has even proven that they may cause loss of structural information. To overcome these problems, in this paper, without decomposition, we represent each n-ary relational fact as a set of its role-value pairs. We further propose a method to conduct Link Prediction on N-ary relational data, thus called NaLP, which explicitly models the relatedness of all the role-value pairs in the same n-ary relational fact. Experimental results validate the effectiveness and merits of the proposed NaLP method.
With a Little Help from My Friends (and Their Friends): Influence Neighborhoods for Social Recommendations
Authors: Avni Gulati, Magdalini Eirinaki
Venue: WWW
Year: 2019

Social recommendations have been a very intriguing domain for researchers in the past decade. The main premise is that the social network of a user can be leveraged to enhance the rating-based recommendation process. This has been achieved in various ways, and under different assumptions about the network characteristics, structure, and availability of other information (such as trust, content, etc.) In this work, we create neighborhoods of influence leveraging only the social graph structure. These are in turn introduced in the recommendation process both as a pre-processing step and as a social regularization factor of the matrix factorization algorithm. Our experimental evaluation using real-life datasets demonstrates the effectiveness of the proposed technique.