OpenRaiser/Intern-Atlas huggingface dataset:
Election trolling: analyzing sentiment in tweets during pakistan elections 2013
Authors: Arjumand Younus, Muhammad Atif Qureshi, Muhammad Saeed, Nasir Touheed, Colm O&apos, Riordan, Gabriella Pasi
Venue: WWW
Year: 2014

The use of Twitter as a discussion platform for political issues has led researchers to study its role in predicting election outcomes. This work studies a much neglected aspect of politics on Twitter namely "election trolling" whereby supporters of different political parties attack each other during election campaigns. We also propose a novel strategy to detect terms that are usually not associated with sentiment but are introduced by supporters of political parties to attack the opposing party. We demonstrate a lack of political maturity as evidenced through high percentage of political attacks in a developing region such as Pakistan.
Evolutionary analysis on online social networks using a social evolutionary game
Authors: Jianye Yu, Yuanzhuo Wang, Xiaolong Jin, Jingyuan Li, Xueqi Cheng
Venue: WWW
Year: 2014

In this paper, we propose a social evolutionary game to investigate the evolution of social networks. Through comparison between simulation and empirical analysis on the social networks of Twitter and Sina Weibo, we validate the effectiveness of the proposed model and estimate the evolutionary phases of the two networks. We find that the users of Sina Weibo can withstand comparatively more costs than the users of Twitter. Therefore, they can perform more positive behavior and consider more about their reputation than Twitter users. Moreover, the evolutionary time of Sina Weibo to a stable state is longer than that of Twitter.
App mining: finding the real value of mobile applications
Authors: Peng Yu, Ching-man Au Yeung
Venue: WWW
Year: 2014

In this poster, we present a new model for estimating the actual value of mobile apps to the users. The model assumes that users are implicitly evaluating the value of the apps in their smartphones when they choose to uninstall some apps. Our proposed method thus makes use of the install and uninstall log in a mobile app store to estimate the value of the apps. Experiments using data from a popular mobile app store show that our model is better in predicting the future download trend of the apps as well as the future uninstallation rate of the apps. We believe such model will be very useful in generating more credible and appropriate mobile app recommendations to users, or in generating features for machine learning systems in more complex prediction tasks.
Inferring international and internal migration patterns from Twitter data
Authors: Emilio Zagheni, Venkata Rama Kiran Garimella, Ingmar Weber, Bogdan State
Venue: WWW
Year: 2014

Data about migration flows are largely inconsistent across countries, typically outdated, and often inexistent. Despite the importance of migration as a driver of demographic change, there is limited availability of migration statistics. Generally, researchers rely on census data to indirectly estimate flows. However, little can be inferred for specific years between censuses and for recent trends. The increasing availability of geolocated data from online sources has opened up new opportunities to track recent trends in migration patterns and to improve our understanding of the relationships between internal and international migration. In this paper, we use geolocated data for about 500,000 users of the social network website "Twitter". The data are for users in OECD countries during the period May 2011- April 2013. We evaluated, for the subsample of users who have posted geolocated tweets regularly, the geographic movements within and between countries for independent periods of four months, respectively. Since Twitter users are not representative of the OECD population, we cannot infer migration rates at a single point in time. However, we proposed a difference-in-differences approach to reduce selection bias when we infer trends in out-migration rates for single countries. Our results indicate that our approach is relevant to address two longstanding questions in the migration literature. First, our methods can be used to predict turning points in migration trends, which are particularly relevant for migration forecasting. Second, geolocated Twitter data can substantially improve our understanding of the relationships between internal and international migration. Our analysis relies uniquely on publicly available data that could be potentially available in real time and that could be used to monitor migration trends. The Web Science community is well-positioned to address, in future work, a number of methodological and substantive questions that we discuss in this article.
Understanding spatial homophily: the case of peer influence and social selection
Authors: Ke Zhang, Konstantinos Pelechrinis
Venue: WWW
Year: 2014

Homophily is a phenomenon observed very frequently in social networks and is related with the inclination of people to be involved with others that exhibit similar characteristics. The roots of homophily can be subtle and are mainly traced back to two mechanisms: (i) social selection and (ii) peer influence. Decomposing the effects of each of these mechanisms requires analysis of longitudinal data. This has been a burden to similar studies in traditional social sciences due to the hardness of collecting such information. However, the proliferation of online social media has enabled the collection of massive amounts of information related with human activities. In this work, we are interested in examining the forces of the above mechanisms in the context of the locations visited by people. For our study, we use a longitudinal dataset collected from Gowalla, a location-based social network (LBSN). LBSNs, unlike other online social media, bond users' online interactions with their activities in real-world, physical locations. Prior work on LBSNs has focused on the influence of geographical constraints on the formation of social ties. On the contrary, in this paper, we perform a microscopic study of the peer influence and social selection mechanisms in LBSNs. Our analysis indicates that while the similarity of friends' spatial trails at a geographically global scale cannot be attributed to peer influence, the latter can explain up to 40% of the geographically localized similarity between friends. Moreover, this percentage depends on the type of locations we examine, and it can be even higher for specific categories (e.g., nightlife spots). Finally, we find that the social selection mechanism, is only triggered by places that exhibit specific network characteristics. We believe that our work can have significant implications on obtaining a deeper understanding of the way that people create friendships, act and move in real space, which can further facilitate and enhance applications such as recommender systems, trip planning and marketing.
Introducing the omega-machine
Authors: Lei Zhang, Thanassis Tiropanis, Wendy Hall, Sung-Hyon Myaeng
Venue: WWW
Year: 2014

In this paper, we propose the Ω-machine model for social machines. By introducing a cluster of "oracles" to a traditional Turing machine, the Ω-machine is capable of describing the interaction between human participants and mechanical machines. We also give two examples of social machines, collective intelligence and rumor spreading, and demonstrate how the general Ω-machine model could be used to simulate their computations.
Who proposed the relationship?: recovering the hidden directions of undirected social networks
Authors: Jun Zhang, Chaokun Wang, Jianmin Wang
Venue: WWW
Year: 2014

Together with the sign (positive or negative) and strength (strong or weak), the directionality is also an important property of social ties, though usually ignored in undirected social networks for its invisibility. However, we believe most social ties are natively directed, and the awareness of directionality can improve our understanding about the network structures and further benefit social network analysis and mining tasks. Thus it's appealing to study whether there exist interesting patterns about directionality in social networks and whether we can learn the directions for undirected networks based on these patterns. In this study, we engage in the investigation of directionality patterns on real-world directed social networks and summarize our findings using four consistency hypotheses. Based on these hypotheses, we propose ReDirect, an optimization framework which makes it possible to infer the hidden directions of undirected social ties based on the network topology only. This general framework can incorporate various predictive models under specific scenarios. Furthermore, we show how to improve ReDirect by introducing semi/self-supervision in the framework and how to construct the self-labeled training data using simple but effective heuristics. Experimental results show that even without external information, our approach can recover the directions of networks effectively.
Query augmentation based intent matching in retail vertical ads
Authors: Huasha Zhao, Wei Vivian Zhang, Ye Chen, John F. Canny, Tak W. Yan
Venue: WWW
Year: 2014

Search advertising shows trends of vertical extension. Vertical ads typically offer better Return of Investment (ROI) to advertisers as a result of better user engagement. However, campaign and bids in vertical ads are not set at the keyword level. As a result, the matching between user query and ads suffers low recall rate and the match quality is heavily impacted by tail queries. In this paper, we propose a retail ads retrieval framework based on query rewrite using personal history data to improve ads recall rate. To insure ads quality, we also present a relevance model for matching rewritten queries with user search intent, with a particular focus on rare queries. Extensive experiments are carried out on large-scale logs collected from the Bing search engine, and results show our system achieves significant gains in ads retrieval rate without compromising ads quality. To our knowledge, this work is the first attempt to leverage user behavioral data in ad matching and apply it to the vertical ads domain.
An upper bound based greedy algorithm for mining top-k influential nodes in social networks
Authors: Chuan Zhou, Peng Zhang, Jing Guo, Li Guo
Venue: WWW
Year: 2014

Influence maximization [4] is NP-hard under the Linear Threshold (LT) model, where a line of greedy algorithms have been proposed. The simple greedy algorithm [4] guarantees accuracy rate of 1-1/e to the optimal solution; the advanced greedy algorithm, e.g., the CELF algorithm [6], runs 700 times faster by exploiting the submodular property of the spread function. However, both models lack efficiency due to heavy Monte-Carlo simulations during estimating the spread function. To this end, in this paper we derive an upper bound for the spread function under the LT model. Furthermore, we propose an efficient UBLF algorithm by incorporating the bound into CELF. Experimental results demonstrate that UBLF, compared with CELF, reduces about 98.9% Monte-Carlo simulations and achieves at least 5 times speed-raising when the size of seed set is small.
Maximizing the long-term integral influence in social networks under the voter model
Authors: Chuan Zhou, Peng Zhang, Wenyu Zang, Li Guo
Venue: WWW
Year: 2014

We address the problem of discovering the influential nodes in social networks under the voter model, which allows multiple activations to the same node, by defining an integral influence maximization problem in a long term. We analyze the problem formulation and present an exact solution to the maximization problem. We also provide a sufficient condition for the convergence of the integral influence. We experimentally compare the exact solution with other heuristic algorithms in the aspects of quality and efficiency.
Temporal capacity graphs for time-varying mobile networks
Authors: Xiangming Zhu, Yong Li, Depeng Jin, Pan Hui
Venue: WWW
Year: 2014

With the rapid emergence of applications in mobile networks, understanding and characterizing their properties becomes extremely important. In this paper, from the fundamental model of time-varying graphs, we introduce Temporal Capacity Graphs (TCG), which characterizes the maximum amount of the data that can be transmitted between any two nodes within any time, and consequently reveals the transmission capacity of the whole network. By applying TCG to several realistic mobile networks, we analyze their unique properties. Moreover, using TCG, we reveal the fundamental relationships and tradeoffs between the mobile network settings and system performance.
Optimizing Display Advertising in Online Social Networks
Authors: Zeinab Abbassi, Aditya Bhaskara, Vishal Misra
Venue: WWW
Year: 2015

Advertising is a significant source of revenue for most online social networks. Conventional online advertising methods need to be customized for online social networks in order to address their distinct characteristics. Recent experimental studies have shown that providing social cues along with ads, e.g. information about friends liking the ad or clicking on an ad, leads to higher click rates. In other words, the probability of a user clicking an ad is a function of the set of friends that have clicked the ad. In this work, we propose formal probabilistic models to capture this phenomenon, and study the algorithmic problem that then arises. Our work is in the context of display advertising where a contract is signed to show an ad to a pre-determined number of users. The problem we study is the following: given a certain number of impressions, what is the optimal display strategy, i.e. the optimal order and the subset of users to show the ad to, so as to maximize the expected number of clicks? Unlike previous models of influence maximization, we show that this optimization problem is hard to approximate in general, and that it is related to finding dense subgraphs of a given size. In light of the hardness result, we propose several heuristic algorithms including a two-stage algorithm inspired by influence-and-exploit strategies in viral marketing. We evaluate the performance of these heuristics on real data sets, and observe that our two-stage heuristic significantly outperforms the natural baselines.
Are We Really Friends?: Link Assessment in Social Networks Using Multiple Associated Interaction Networks
Authors: Mohammed Abufouda, Katharina A. Zweig
Venue: WWW
Year: 2015

Many complex network systems suffer from noise that disguises the structure of the network and hinders an accurate analysis of these systems. Link assessment is the process of identifying and eliminating the noise from network systems in order to better understand these systems. In this paper, we address the link assessment problem in social networks that may suffer from noisy relationships. We employed a machine learning classifier for assessing the links in the social network of interest using the data from the associated interaction networks around it. The method was tested with two different data sets: each contains the social network of interest, with ground truth, along with the associated interaction networks. The results showed that it is possible to effectively assess the links of a social network using only the structure of a single network of the associated interaction networks and also using the structure of the whole set of the associated interaction networks. The experiment also revealed that the assessment performance using only the structure of the social network of interest is relatively less accurate than using the associated interaction networks. This indicates that link formation in the social network of interest is not only driven by the internal structure of the social network, but also influenced by the external factors provided in the associated interaction networks.
Important Events in the Past, Present, and Future
Authors: Abdalghani Abujabal, Klaus Berberich
Venue: WWW
Year: 2015

We address the problem of identifying important events in the past, present, and future from semantically-annotated large-scale document collections. Semantic annotations that we consider are named entities (e.g., persons, locations, organizations) and temporal expressions (e.g., during the 1990s). More specifically, for a given time period of interest, our objective is to identify, rank, and describe important events that happened. Our approach P2F Miner makes use of frequent itemset mining to identify events and group sentences related to them. It uses an information-theoretic measure to rank identified events. For each of them, it selects a representative sentence as a description. Experiments on ClueWeb09 using events listed in Wikipedia year articles as ground truth show that our approach is effective and outperforms a baseline based on statistical language models.
A Hybrid Approach to Perform Efficient and Effective Query Execution Against Public SPARQL Endpoints
Authors: Maribel Acosta
Venue: WWW
Year: 2015

Linked Open Data initiatives have fostered the publication of Linked Data sets, as well as the deployment of publicly available SPARQL endpoints as client-server querying infrastructures to access these data sets. However, recent studies reveal that SPARQL endpoints may exhibit significant limitations in supporting real-world applications, and public linked data sets can suffer of quality issues, e.g., data can be incomplete or incorrect. We tackle these problems and propose a novel hybrid architecture that relies on shipping policies to improve the performance of SPARQL endpoints, and exploits human and machine query processing computation to enhance the quality of Linked Data sets. We report on initial empirical results that suggest that the proposed techniques overcome current drawbacks, and may provide a novel solution to make these promising infrastructures available for real-world applications.
Who are the American Vegans related to Brad Pitt?: Exploring Related Entities
Authors: Nitish Aggarwal, Kartik Asooja, Housam Ziad, Paul Buitelaar
Venue: WWW
Year: 2015

In this demo, we present Entity Relatedness Graph (EnRG), a focused related entities explorer, which provides the users with a dynamic set of filters and facets. It gives a ranked lists of related entities to a given entity, and clusters them using the different filters. For instance, using EnRG, one can easily find the American vegans related to Brad Pitt or Irish universities related to Semantic Web. Moreover, EnRG helps a user in discovering the provenance for implicit relations between two entities. EnRG uses distributional semantics to obtain the relatedness scores between two entities.
A Study of Distinctiveness in Web Results of Two Search Engines
Authors: Rakesh Agrawal, Behzad Golshan, Evangelos E. Papalexakis
Venue: WWW
Year: 2015

Google and Bing have emerged as the diarchy that arbitrates what documents are seen by Web searchers, particularly those desiring English language documents. We seek to study how distinctive are the top results presented to the users by the two search engines. A recent eye-tracking has shown that the web searchers decide whether to look at a document primarily based on the snippet and secondarily on the title of the document on the web search result page, and rarely based on the URL of the document. Given that the snippet and title generated by different search engines for the same document are often syntactically different, we first develop tools appropriate for conducting this study. Our empirical evaluation using these tools shows a surprising agreement in the results produced by the two engines for a wide variety of queries used in our study. Thus, this study raises the open question whether it is feasible to design a search engine that would produce results distinct from those produced by Google and Bing that the users will find helpful.
Serving Ads to "Yahoo Answers" Occasional Visitors
Authors: Michal Aharon, Amit Kagian, Yohay Kaplan, Raz Nissim, Oren Somekh
Venue: WWW
Year: 2015

Modern ad serving systems can benefit when allowed to accumulate user information and use it as part of the serving algorithm. However, this often does not coincide with how the web is used. Many domains will see users for only brief interactions, as users enter a domain through a search result or social media link and then leave. Having access to little or no user information and no ability to assemble a user profile over a prolonged period of use, we would still like to leverage the information we have to the best of our ability. In this paper we attempt several methods of improving ad serving for occasional users, including leveraging user information that is still available, content analysis of the page, information about the page's content generators and historical breakdown of visits to the page. We compare and combine these methods in a framework of a collaborative filtering algorithm, test them on real data collected from Yahoo Answers, and achieve significant improvements over baseline algorithms.
A Taxonomy of Crowdsourcing Campaigns
Authors: Majid Ali AlShehry, Bruce Walker Ferguson
Venue: WWW
Year: 2015

Crowdsourcing serves different needs of different sets of users. Most existing definitions and taxonomies of crowdsourcing address platform purpose while paying little attention to other parameters of this novel social phenomenon. In this paper, we analyze 41 crowdsourcing campaigns on 21 crowdsourcing platforms to derive 9 key parameters of successful crowdsourcing campaigns and introduce a comprehensive taxonomy of crowdsourcing. Using this taxonomy, we identify crowdsourcing trends in two parameters, platform purpose and contributor motivation. The paper highlights important advantages of using this conceptual model in planning crowdsourcing campaigns and concludes with a discussion of emerging challenges to such campaigns.
The World Conversation: Web Page Metadata Generation From Social Sources
Authors: Omar Alonso, Sushma Bannur, Kartikay Khandelwal, Shankar Kalyanaraman
Venue: WWW
Year: 2015

Over the past couple of years, social networks such as Twitter and Facebook have become the primary source for consuming information on the Internet. One of the main differentiators of this content from traditional information sources available on the Web is the fact that these social networks surface individuals' perspectives. When social media users post and share updates with friends and followers, some of those short fragments of text contain a link and a personal comment about the web page, image or video. We are interested in mining the text around those links for a better understanding of what people are saying about the object they are referring to. Capturing the salient keywords from the crowd is rich metadata that we can use to augment a web page. This metadata can be used for many applications like ranking signals, query augmentation, indexing, and for organizing and categorizing content. In this paper, we present a technique called social signatures that given a link to a web page, pulls the most important keywords from the social chatter around it. That is, a high level representation of the web page from a social media perspective. Our findings indicate that the content of social signatures differs compared to those from a web page and therefore provides new insights. This difference is more prominent as the number of link shares increase. To showcase our work, we present the results of processing a dataset that contains around 1 Billion unique URLs shared in Twitter and Facebook over a two month period. We also provide data points that shed some light on the dynamics of content sharing in social media.