OpenRaiser/Intern-Atlas huggingface dataset:
How to Improve the Answering Effectiveness in Pay-for-Knowledge Community: An Exploratory Application of Intelligent QA System
Authors: Yihang Cheng, Xi Zhang, Hao Wang, Shang Jiang
Venue: WWW
Year: 2018

Community Question Answering (CQA) has emerged recently and it becomes popular among people. During the process of the communication, different knowledge can be merged. Recently, several vendors use the business model of paying for knowledge to make these knowledge to the monetary benefits, then the Pay-for-Knowledge Communities (PKC) have been applied. Even PKC has interesting business model, there are several problems to be solved, and one of the most salient problem is that questioners may takes too long time to choose the most valuable answers, leading to questioners not able to pay for suitable answerers and many problems about platforms' operation There are several previous research has focused on this problem but still have not found satisfactory solutions as questions and answers are more and more complex in PKC platforms. With the development of cognitive computing techniques, applying an intelligent QA system in PKC to improve the answering effectiveness may be possible. In this paper, we tried to investigate how to apply the intelligent QA system into PKC platform to improve the answering effectiveness. For solving the problems of matching complex questions and answers, we present a Four Module QA Model based on the normal intelligent QA System. Compared to normal intelligent QA System, our model uses categories to classify the questions with traditional machine learning methods. We use answers in each category of corresponding questions as one dataset, answers in each entity of corresponding question as the other dataset, finally, these two datasets make up the document database. Then we got the best answer among past answers through comparing the TF-IDF weighted bag-of word vectors of two datasets or the new answer including key words through Long Short-Term Memory (LSTM) algorithm with PKC's features composed of centrality and money. Experiments were developed on a dataset with 1222 users' QA sites collected from a QA community. The model we proposed is expected to increase QA's effectiveness and improve the business model of Pay-for-Knowledge Communities.
WITH: Human-Computer Collaboration for Data Annotation and Enrichment
Authors: Alexandros Chortaras, Anna Christaki, Nasos Drosopoulos, Eirini Kaldeli, Maria Ralli, Anastasia Sofou, Arne Stabenau, Giorgos Stamou, Vassilis Tzouvaras
Venue: WWW
Year: 2018

The transformation that has been accomplished in Cultural Heritage (CH) during the last decades has resulted in the production of vast amounts of content from many different cultural institutions, such as museums, libraries and archives. A large part of this rich content has been aggregated in digital platforms that serve as cross-domain hubs, which however offer limited usability and accessibility of content due to insufficient data and metadata quality. In our effort to make CH more accessible and reusable, we introduce WITH, an aggregation platform that provides enhanced services and enables human-computer collaboration for data annotations and enrichment. WITH excels existing cultural content aggregation platforms by advancing digital cultural data through the combination of artificial intelligence automation and creative user engagement, thus facilitating its accessibility, visibility, and re-use. In particular, by using image and free text analysis methodologies for automatic metadata enrichment, in accordance to the human expertise for enrichment and validation through crowdsourcing approaches with gamification elements, WITH combines the intelligence of humans and computers to improve the quality of digital cultural content and its presentation, establishing new ways of collaboration between cultural organizations and their audiences.
VIZ-Wiki: Generating Visual Summaries to Factoid Threads in Community Question Answering Services
Authors: Tanya Chowdhury, Aashay Mittal, Tanmoy Chakraborty
Venue: WWW
Year: 2018

In this demo, we present VIZ-Wiki, a browser extension which generates an overview of summarizable threads in Question Answering forums. It reduces a user's effort to go through lengthy text-based, sarcastic and highly critiqued answers. Our tool can be used to collect community opinion from popular discussion sites like Quora, Yahoo! Answers, Reddit etc. as well as topic-centric ones such as Askubuntu, Stackoverflow. We rely on textual information of these forums to extract insightful summaries for a reader. VIZ-Wiki provides users a pie-graph view marking popular choices when such a question link is raised. A button guides them to detailed statistics and relevant list of answers. It further highlights sentences relevant to an answer choice in the text. VIZ-Wiki deals with answers contradicted by other users, prioritizes highly-recommended ones and avoids sarcasm. We test our model on the factoid questions dataset of Yahoo! Answers and obtain a macro precision of 0.6 on displayed answers and a macro recall of 0.69, beating the baseline significantly. To the best of our knowledge, VIZ-Wiki is the first attempt to visualize answers for questions in community question answering services. In the spirit of reproducibility, we have released the code and a demonstration video public at \urlhttp://goo.gl/cyx3EF and \urlhttps://youtu.be/XNmRa_jtmC8 respectively
Journalism, Misinformation and Fact Checking Chairs' Welcome & Organization
Authors: Giovanni Luca Ciampaglia, Kristina Lerman, Panagiotis Metaxas
Venue: WWW
Year: 2018

It is our pleasure to welcome you to the WWW 2018 Journalism, Misinformation and Fact Checking Alternate Track. Although the problem of misinformation and deceptive information is as old as Web itself, the topic has gained a lot of attention recently. Phenomena, such as misinformation propagation, fabricated news reports (also known as "fake news",) computational propaganda, astroturf, and ideological polarization have become more common on the Web and the social Web, calling for a cross-cutting approach to better understand the topic. One approach that has gained some traction is that of the establishment of fact-checking organizations.
Do Violent People Smile: Social Media Analysis of their Profile Pictures
Authors: Mauro Coletto, Claudio Lucchese, Salvatore Orlando
Venue: WWW
Year: 2018

The popularity of online social platforms has also determined the emergence of violent and abusive behaviors reflecting real life issues into the digital arena. Cyberbullying, Internet banging, pedopornography, sexting are examples of these behaviors, as witnessed in the social media environments. Several studies have shown how to approximately detect those behaviors by analyzing the social interactions and in particular the content of the exchanged messages. The features considered in the models basically include detection of o ensive language through NLP techniques and vocabularies, social network structural measures and, if available, user context information. Our goal is to investigate those users who adopt offensive language and hate speech in Twitter by analyzing their profile pictures. Results show that violent people smile less and they are dominating by anger, fear and sadness.
Optimizing Ad Refresh In Mobile App Advertising
Authors: Florin Constantin, Christopher Harris, Samuel Ieong, Aranyak Mehta, Xi Tan
Venue: WWW
Year: 2018

In-app advertising is a complex market worth billions of dollars per year, yet it has been studied significantly less than traditional web display ads. In this paper we study an important but often overlooked feature of ads in mobile apps (mostly absent in traditional web ads), that of ad refreshes : A user is shown a stream of banner ads during the app session, in which each ad is displayed in the ad slot for a certain amount of time (the refresh rate) before the ad-slot is refreshed to the next ad. Data analysis on our large-scale experiments that vary refresh rates reveals a surprising result, that cannot be explained by existing user click models: Varying ads» refresh almost preserves total number of clicks. We propose a new, natural, "two-phase" click model for this setting that explains this independence, as well as our measurements of the click-through rate as a function of the impression»s time-on-screen and of ad-repeat counts. The new click model leads to a clean formulation of the problem of auctioning the entire user-session: i.e., determining online, both the sequence of winning ads as well as the amount of time to display each one. We complement the theoretical auction design with results from a live-traffic experiment with its implementation. Our experiments and analysis provide the theoretical foundation for AdMob»s "Google-optimized refresh rate" feature, used by many mobile apps for better monetization of ads shown to millions of users.
Pitfalls of Affective Computing: How can the automatic visual communication of emotions lead to harm, and what can be done to mitigate such risks
Authors: Martin Cooney, Sepideh Pashami, Anita Pinheiro Sant&apos, Anna, Yuantao Fan, Slawomir Nowaczyk
Venue: WWW
Year: 2018

What would happen in a world where people could "see'' others' hidden emotions directly through some visualizing technology Would lies become uncommon and would we understand each other better Or to the contrary, would such forced honesty make it impossible for a society to exist The science fiction television show Black Mirror has exposed a number of darker scenarios in which such futuristic technologies, by blurring the lines of what is private and what is not, could also catalyze suffering. Thus, the current paper first turns an eye towards identifying some potential pitfalls in emotion visualization which could lead to psychological or physical harm, miscommunication, and disempowerment. Then, some countermeasures are proposed and discussed--including some level of control over what is visualized and provision of suitably rich emotional information comprising intentions--toward facilitating a future in which emotion visualization could contribute toward people's well-being. The scenarios presented here are not limited to web technologies, since one typically thinks about emotion recognition primarily in the context of direct contact. However, as interfaces develop beyond today's keyboard and monitor, more information becomes available also at a distance--for example, speech-to-text software could evolve to annotate any dictated text with a speaker's emotional state.
From Reaction to Proaction: Unexplored Ways to the Detection of Evolving Spambots
Authors: Stefano Cresci, Marinella Petrocchi, Angelo Spognardi, Stefano Tognazzi
Venue: WWW
Year: 2018

We envisage a revolutionary change in the approach to spambot detection: instead of taking countermeasures only after having collected evidence of new spambot mischiefs, in a near future techniques will be able to anticipate the ever-evolving spammers.
Minimizing Latency in Online Ride and Delivery Services
Authors: Abhimanyu Das, Sreenivas Gollapudi, Anthony Kim, Debmalya Panigrahi, Chaitanya Swamy
Venue: WWW
Year: 2018

Motivated by the popularity of online ride and delivery services, we study natural variants of classical multi-vehicle minimum latency problems where the objective is to route a set of vehicles located at depots to serve requests located on a metric space so as to minimize the total latency. In this paper, we consider point-to-point requests that come with source-destination pairs and release-time constraints that restrict when each request can be served. The point-to-point requests and release-time constraints model taxi rides and deliveries. For all the variants considered, we show constant-factor approximation algorithms based on a linear programming framework. To the best of our knowledge, these are the first set of results for the aforementioned variants of the minimum latency problems. Furthermore, we provide an empirical study of heuristics based on our theoretical algorithms on a real data set of taxi rides.
Region-wise Ranking of Sports Players based on Link Fusion
Authors: Ali Daud, Akbar Hussain, Rabeeh Ayaz Abbasi, Naif Radi Aljohani, Tehmina Amjad, Hassan Dawood
Venue: WWW
Year: 2018

Players are ranked in various sports to show their importance over other players. Existing methods only consider intra-type links (e.g., player to player and team to team), but ignore inter-type links (e.g., one type of player to another type of player, such as batsman to bowler and player to team) based on cognitive aspects. They also ignore the spatiality of the players. There is a strong relationship among players and their teams, which can be represented as a network consisting of multi-type interrelated objects. In this paper, we propose a players' ranking method, called Region-wise Players Link Fusion (RPLF) which is applied to the sport of cricket. RPLF considers players' region-wise intra-type and inter-type relation-based features to rank the players. Considering multi-type interrelated objects is based on the intuition that a batsman scoring high against top bowlers of a strong team or a bowler taking wickets against top batsmen of a strong team is considered as a good player. The experimental results show that RPLF provides promising insights of players' rankings. RLFP is a generic method and can be applied to different sports for ranking players.
Demarcating Endogenous and Exogenous Opinion Diffusion Process on Social Networks
Authors: Abir De, Sourangshu Bhattacharya, Niloy Ganguly
Venue: WWW
Year: 2018

The networked opinion diffusion in online social networks (OSN) is governed by the two genres of opinions-endogenous opinions that are driven by the influence of social contacts between users, and exogenous opinions which are formed by external effects like news, feeds etc. Such duplex opinion dynamics is led by users belonging to two categories- organic users who generally post endogenous opinions and extrinsic users who are susceptible to externalities, and mostly post the exogenous messages. Precise demarcation of endogenous and exogenous messages offers an important cue to opinion modeling, thereby enhancing its predictive performance. On the other hand, accurate user selection aids to detect extrinsic users, which in turn helps in opinion shaping. In this paper, we design CherryPick, a novel learning machinery that classifies the opinions and users by solving a joint inference task in message and user set, from a temporal stream of sentiment messages. Furthermore, we validate the efficacy of our proposal from both modeling and shaping perspectives. Moreover, for the latter, we formulate the opinion shaping problem in a novel framework of stochastic optimal control, in which the selected extrinsic users optimally post exogenous messages so as to guide the opinions of others in a desired way. On five datasets crawled from Twitter, CherryPick offers a significant accuracy boost in terms of opinion forecasting, against several competitors. Furthermore, it can precisely determine the quality of a set of control users, which together with the proposed online shaping strategy, consistently steers the opinion dynamics more effectively than several state-of-the-art baselines.
Learning to Recognize Musical Genre from Audio: Challenge Overview
Authors: Michaël Defferrard, Sharada P. Mohanty, Sean F. Carroll, Marcel Salathé
Venue: WWW
Year: 2018

We here summarize our experience running a challenge with open data for musical genre recognition. Those notes motivate the task and the challenge design, show some statistics about the submissions, and present the results.
Increasing Transparency through Web Maps
Authors: Auriol Degbelo, Tomi Kauppinen
Venue: WWW
Year: 2018

Recent years have witnessed progress of public institutions in making their datasets available online, free of charge, for re-use. This notwithstanding, there is still a long way to go to put the power of data in the hands of citizens. This article suggests that transparency in the context of open government can be increased through web maps featuring: i) Application Programming Interfaces (APIs) which support app and data usage tracking; and (ii) 'transparency badges' which inform the users about the presence/absence of extra, useful contextual information. Eight examples of web maps are introduced as proof of concept for the idea. Designing and implementing these web maps has reminded of the need of interactive guidelines to help non-experts select vocabularies, and datasets to link to. The ideas presented are relevant to making existing open data more user friendly (and ultimately more usable).
Buzz in Social Media: Detection of Short-lived Viral Phenomena
Authors: Clemens Deusser, Nora Jansen, Jan Reubold, Benjamin Schiller, Oliver Hinz, Thorsten Strufe
Venue: WWW
Year: 2018

Social media interaction happens in a broad variety of context and magnitude. The vast majority of posts cause little to no discussion, while some start trends and become viral. We study the virality, explicitly of "Buzzes" - posts that evoke intense interaction over a short period of time, as they have been observed frequently, some- times with severe consequences for individuals and companies in the physical world. Early detection of a Buzz may help mitigate or prevent negative consequences of large scale social media outrage against companies or persons, by giving them a chance to react at an early stage. Collecting a labeled set of over 100,000 posts on Facebook pages, we first explore properties that define a Buzz using logistic regres- sion. This method helps us to interpret the results and derive prac- tical recommendations. We subsequently train classifiers and apply machine learning based classification techniques to demonstrate the potential capabilities of automated prediction. We achieve high recall with moderate precision, where feature boosting on broad feature sets yields the most promising results. Our study reveals that Buzzes are well described by a high num- ber of comments from previously passive users, a high number of likes given to comments, and a prolonged discussion period - properties that can be used to distinguish inconsequential posts from potentially volatile ones.
Social Sensing and Enterprise Intelligence: Towards a Smart Enterprise Transformation (SSEI 2018) Chairs' Welcome and Organization
Authors: Lipika Dey, Tirthankar Dasgupta, Priyanka Sinha
Venue: WWW
Year: 2018

Breakthroughs in Artificial Intelligence (AI) and World Wide Web technologies have opened a new direction in Enterprise Intelligence, that is gradually transforming the way enterprises perform business and interact with their customers. This change is largely driven by the widespread consumer adoption of sophisticated AI technologies and web based social media. Consequently, almost all business enterprises face a number of challenges such as adoption of new business paradigms; customer centric business processes; issues with large, multi-modal, multilingual, and multicultural data, analyzing behavioral signals from social media; agility, security and many more. Therefore, the primary goal of this workshop is to bring together industry professionals and researchers working in the area of AI, Natural Language Processing (NLP), Machine-Learning, linguistics, social science, HCI, design and computer vision and those whose work concerns the intersection of these areas, together to provide a venue for the multidisciplinary discussion of how ubiquitous AI technologies can help in extracting social and enterprise intelligence for smart enterprise transformation while addressing the aforementioned challenges.
A Natural Language Driven Approach for Automated Web API Development: Gherkin2OAS
Authors: Anastasios Dimanidis, Kyriakos C. Chatzidimitriou, Andreas L. Symeonidis
Venue: WWW
Year: 2018

Speeding up the development process of Web Services, while adhering to high quality software standards is a typical requirement in the software industry. This is why industry specialists usually suggest "driven by" development approaches to tackle this problem. In this paper, we propose such a methodology that employs Specification Driven Development and Behavior Driven Development in order to facilitate the phases of Web Service requirements elicitation and specification. Furthermore, we introduce gherkin2OAS, a software tool that aspires to bridge the aforementioned development approaches. Through the suggested methodology and tool, one may design and build RESTful services fast, while ensuring proper functionality.
An Improved Sampler for Bayesian Personalized Ranking by Leveraging View Data
Authors: Jingtao Ding, Fuli Feng, Xiangnan He, Guanghui Yu, Yong Li, Depeng Jin
Venue: WWW
Year: 2018

Bayesian Personalized Ranking (BPR) is a representative pairwise learning method for optimizing recommendation models. It is widely known that the performance of BPR depends largely on the quality of the negative sampler. In this short paper, we make two contributions with respect to BPR. First, we find that sampling negative items from the whole space is unnecessary and may even degrade the performance. Second, focusing on the purchase feedback of the E-commerce domain, we propose a simple yet effective sampler for BPR by leveraging the additional view data. Compared to the vanilla BPR that applies a uniform sampler on all candidates, our view-aware sampler enhances BPR with a relative improvement of 27.36% and 69.54% on two real-world datasets respectively.
Geographical Feature Extraction for Entities in Location-based Social Networks
Authors: Daizong Ding, Mi Zhang, Xudong Pan, Duocai Wu, Pearl Pu
Venue: WWW
Year: 2018

Location-based embedding is a fundamental problem to solve in location-based social networks (LBSN). In this paper, we propose a geographical convolutional neural tensor network (GeoCNTN) as a generic embedding model. GeoCNTN first takes the raw location data and extracts from it a more well-conditioned representation by our proposed Geo-CMeans algorithm. We then use a convolutional neural network (CNN) and an embedding structure to extract individual latent structural patterns from the preprocessed data. Finally, we apply a neural tensor network (NTN) to craft the implicitly related features we have obtained into a unified geographical feature. The advantages of our GeoCNTN mainly come from its novel neural network structure, which intrinsically offers a mechanism to extract latent structural features from the geographical data, as well as its wide applicability in various LBSN-related tasks. From two case studies, i.e. link prediction and entity classification in user-group LBSN, we evaluate the embedding efficacy of our model. Results show that GeoCNTN significantly performs better on at least two tasks, with improvement by 9% w.r.t. NDCG and 11% w.r.t. F1 score respectively, using the Meetup-USA dataset.