OpenRaiser/Intern-Atlas huggingface dataset:
Cognos: Crowdsourcing search for topic experts in microblogs
Authors: Saptarshi Ghosh, Naveen Kumar Sharma, Fabrício Benevenuto, Niloy Ganguly, P. Krishna Gummadi
Venue: Conference SIGIR 2012
Year: 2012

Finding topic experts on microblogging sites with millions of users, such as Twitter, is a hard and challenging problem. In this paper, we propose and investigate a new methodology for discovering topic experts in the popular Twitter social network. Our methodology relies on the wisdom of the Twitter crowds -- it leverages Twitter Lists, which are often carefully curated by individual users to include experts on topics that interest them and whose meta-data (List names and descriptions) provides valuable semantic cues to the experts' domain of expertise. We mined List information to build Cognos, a system for finding topic experts in Twitter. Detailed experimental evaluation based on a real-world deployment shows that: (a) Cognos infers a user's expertise more accurately and comprehensively than state-of-the-art systems that rely on the user's bio or tweet content, (b) Cognos scales well due to built-in mechanisms to efficiently update its experts' database with new users, and (c) Despite relying only on a single feature, namely crowdsourced Lists, Cognos yields results comparable to, if not better than, those given by the official Twitter experts search engine for a wide range of queries in user tests. Our study highlights Lists as a potentially valuable source of information for future content or expert search systems in Twitter.
Mining topic-level opinion influence in microblog
Authors: Daifeng Li, Xin Shuai, Gordon Guo-Zheng Sun, Jie Tang, Ying Ding, Zhipeng Luo
Venue: Conference CIKM 2012
Year: 2012

This paper proposes a Topic-Level Opinion Influence Model (TOIM) that simultaneously incorporates topic factor, user opinions and social influence in a unified probabilistic model with two stages learning processes. In the first stage, topic factor and user influence are integrated to generate users' influential relationship based on different topics; in the second stage, users' historical messages and social interaction records are leveraged by TOIM to construct their historical opinions and neighbors' opinion influence through a statistical learning process, which can be further utilized to predict users' future opinions on some specific topics. We evaluate our TOIM on a large-scaled dataset from Tencent Weibo, one of the largest microbloggings website in China. The experimental results show that TOIM can better predict users' opinion than other baseline methods.
Absence time and user engagement: evaluating ranking functions
Authors: Georges Dupret, Mounia Lalmas
Venue: Conference WSDM 2013
Year: 2013

In the online industry, user engagement is measured with various engagement metrics used to assess users' depth of engagement with a website. Widely-used metrics include clickthrough rates, page views and dwell time. Relying solely on these metrics can lead to contradictory if not erroneous conclusions regarding user engagement. In this paper, we propose the time between two user visits, or the absence time, to measure user engagement. Our assumption is that if users find a website interesting, engaging or useful, they will return to it sooner -a reflection of their engagement with the site -than if this is not the case. This assumption has the advantage of being simple and intuitive and applicable to a large number of settings. As a case study, we use a community Q&A website, and compare the behaviour of users exposed to six functions used to rank past answers, both in terms of traditional metrics and absence time. We use Survival Analysis to show the relation between absence time and other engagement metrics. We demonstrate that the absence time leads to coherent, interpretable results and helps to better understand other metrics commonly used to evaluate user engagement in search.
Using the wisdom of the crowds for keyword generation
Authors: Ariel Fuxman, Panayiotis Tsaparas, Kannan Achan, Rakesh Agrawal
Venue: Conference WWW 2008
Year: 2008

In the sponsored search model, search engines are paid by businesses that are interested in displaying ads for their site alongside the search results. Businesses bid for keywords, and their ad is displayed when the keyword is queried to the search engine. An important problem in this process is keyword generation: given a business that is interested in launching a campaign, suggest keywords that are related to that campaign. We address this problem by making use of the query logs of the search engine. We identify queries re-lated to a campaign by exploiting the associations between queries and URLs as they are captured by the user’s clicks. These queries form good keyword suggestions since they cap-ture the “wisdom of the crowd ” as to what is related to a site. We formulate the problem as a semi-supervised learn-ing problem, and propose algorithms within the Markov Random Field model. We perform experiments with real query logs, and we demonstrate that our algorithms scale to large query logs and produce meaningful results.
Dynamic ranked retrieval
Authors: Christina Brandt, Thorsten Joachims, Yisong Yue, Jacob Bank
Venue: Conference WSDM 2011
Year: 2011

We present a theoretically well-founded retrieval model for dynamically generating rankings based on interactive user feedback. Unlike conventional rankings that remain static after the query was issued, dynamic rankings allow and anticipate user activity, thus providing a way to combine the otherwise contradictory goals of result diversification and high recall. We develop a decision-theoretic framework to guide the design and evaluation of algorithms for this interactive retrieval setting. Furthermore, we propose two dynamic ranking algorithms, both of which are computationally efficient. We prove that these algorithms provide retrieval performance that is guaranteed to be at least as good as the optimal static ranking algorithm. In empirical evaluations, dynamic ranking shows substantial improvements in retrieval performance over conventional static rankings.
The life and death of online groups: Predicting group growth and longevity
Authors: Sanjay Ram Kairam, Dan J. Wang, Jure Leskovec
Venue: Conference WSDM 2012
Year: 2012

We pose a fundamental question in understanding how to identify and design successful communities: What factors predict whether a community will grow and survive in the long term? Social scientists have addressed this question extensively by analyzing offline groups which endeavor to attract new members, such as social movements, finding that new individuals are influenced strongly by their ties to members of the group. As a result, prior work on the growth of communities has treated growth primarily as a diffusion processes, leading to findings about group evolution which can be difficult to explain. The proliferation of online social networks and communities, however, has created new opportunities to study, at a large scale and with very fine resolution, the mechanisms which lead to the formation, growth, and demise of online groups.
Twitcident: Fighting Fire with Information from Social Web Streams
Authors: Fabian Abel, Claudia Hauff, Geert-Jan Houben, Richard Stronkman, Ke Tao
Venue: Conference WWW 2012
Year: 2012

In this paper, we present Twitcident, a framework and Web-based system for filtering, searching and analyzing information about real-world incidents or crises. Twitcident connects to emergency broadcasting services and automatically starts tracking and filtering information from Social Web streams (Twitter) when a new incident occurs. It enriches the semantics of streamed Twitter messages to profile incidents and to continuously improve and adapt the information filtering to the current temporal context. Faceted search and analytical tools allow users to retrieve particular information fragments and overview and analyze the current situation as reported on the Social Web. Demo: http://wis.ewi.tudelft.nl/twitcident/
Emerging topic detection for organizations from microblogs
Authors: Yan Chen, Hadi Amiri, Zhoujun Li, Tat-Seng Chua
Venue: Conference SIGIR 2013
Year: 2013

Microblog services have emerged as an essential way to strengthen the communications among individuals and organizations. These services promote timely and active discussions and comments towards products, markets as well as public events, and have attracted a lot of attentions from organizations. In particular, emerging topics are of immediate concerns to organizations since they signal current concerns of, and feedback by their users. Two challenges must be tackled for effective emerging topic detection. One is the problem of real-time relevant data collection and the other is the ability to model the emerging characteristics of detected topics and identify them before they become hot topics. To tackle these challenges, we first design a novel scheme to crawl the relevant messages related to the designated organization by monitoring multi-aspects of microblog content, including users, the evolving keywords and their temporal sequence. We then develop an incremental clustering framework to detect new topics, and employ a range of content and temporal features to help in promptly detecting hot emerging topics. Extensive evaluations on a representative real-world dataset based on Twitter data demonstrate that our scheme is able to characterize emerging topics well and detect them before they become hot topics.
Studying How the Past is Remembered: Towards Computational History Through Large Scale Text Mining
Authors: Ching-man Au Yeung, Adam Jatowt
Venue: Conference CIKM 2011
Year: 2011

History helps us understand the present and even to predict the future to certain extent. Given the huge amount of data about the past, we believe computer science will play an increasingly important role in historical studies, with computational history becoming an emerging interdisciplinary field of research. We attempt to study how the past is remembered through large scale text mining. We achieve this by first collecting a large dataset of news articles about different countries and analyzing the data using computational and statistical tools. We show that analysis of references to the past in news articles allows us to gain a lot of insight into the collective memories and societal views of different countries. Our work demonstrates how various computational tools can assist us in studying history by revealing interesting topics and hidden correlations. Our ultimate objective is to enhance history writing and evaluation with the help of algorithmic support.
Extracting collective expectations about the future from large text collections
Authors: Adam Jatowt, Ching-man Au Yeung
Venue: Conference CIKM 2011
Year: 2011

News articles often contain information about the future. Given the huge volume of information available nowadays, an automatic way for extracting and summarizing future-related information is desirable. Such information will allow people to obtain a collective image of the future, to recognize possible future scenarios and be prepared for the future events. We propose a model-based clustering algorithm for detecting future events based on information extracted from a text corpus. The algorithm takes into account both textual and temporal similarity of sentences. We demonstrate that our algorithm can be used to discover future events and estimate their probabilities over time.
Learning to Select a Time-aware Retrieval Model
Authors: Nattiya Kanhabua, Klaus Berberich, Kjetil Nørvåg
Venue: Conference SIGIR 2012
Year: 2012

Time-aware retrieval models exploit one of two time dimensions, namely, (a) publication time or (b) content time (temporal expressions mentioned in documents). We show that the effectiveness for a temporal query (e.g., illinois earthquake 1968) depends significantly on which time dimension is factored into ranking results. Motivated by this, we propose a machine learning approach to select the most suitable time-aware retrieval model for a given temporal query. Our method uses three classes of features obtained from analyzing distributions over two time dimensions, a distribution over terms, and retrieval scores within top-k result documents. Experiments on real-world data with crowdsourced relevance assessments show the potential of our approach.
Understanding the Diversity of Tweets in the Time of Outbreaks
Authors: Nattiya Kanhabua, Wolfgang Nejdl
Venue: Conference WWW 2013
Year: 2013

A microblogging service like Twitter continues to surge in importance as a means of sharing information in social networks. In the medical domain, several works have shown the potential of detecting public health events (i.e., infectious disease outbreaks) using Twitter messages or tweets. Given its real-time nature, Twitter can enhance early outbreak warning for public health authorities in order that a rapid response can take place. Most of previous works on detecting outbreaks in Twitter simply analyze tweets matched disease names and/or locations of interests. However, the effectiveness of such method is limited for two main reasons. First, disease names are highly ambiguous, i.e., referring slangs or non health-related contexts. Second, the characteristics of infectious diseases are highly dynamic in time and place, namely, strongly time-dependent and vary greatly among different regions. In this paper, we propose to analyze the temporal diversity of tweets during the known periods of real-world outbreaks in order to gain insight into a temporary focus on specific events. More precisely, our objective is to understand whether the temporal diversity of tweets can be used as indicators of outbreak events, and to which extent. We employ an efficient algorithm based on sampling to compute the diversity statistics of tweets at particular time. To this end, we conduct experiments by correlating temporal diversity with the estimated event magnitude of 14 real-world outbreak events manually created as ground truth. Our analysis shows that correlation results are diverse among different outbreaks, which can reflect the characteristics (severity and duration) of outbreaks.
Social networks under stress
Authors: Daniel M. Romero, Brian Uzzi, Jon M. Kleinberg
Venue: Conference WWW 2016
Year: 2016

Social network research has begun to take advantage of fine-grained communications regarding coordination, decision-making, and knowledge sharing. These studies, however, have not generally analyzed how external events are associated with a social network's structure and communicative properties. Here, we study how external events are associated with a network's change in structure and communications. Analyzing a complete dataset of millions of instant messages among the decision-makers in a large hedge fund and their network of outside contacts, we investigate the link between price shocks, network structure, and change in the affect and cognition of decision-makers embedded in the network. When price shocks occur the communication network tends not to display structural changes associated with adaptiveness. Rather, the network 'turtles up'. It displays a propensity for higher clustering, strong tie inter- action, and an intensification of insider vs. outsider communication. Further, we find changes in network structure pre- dict shifts in cognitive and affective processes, execution of new transactions, and local optimality of transactions better than prices, revealing the important predictive relationship between network structure and collective behavior within a social network.
D-cube: Dense-block detection in terabyte-scale tensors
Authors: Kijung Shin, Bryan Hooi, Jisu Kim, Christos Faloutsos
Venue: Conference WSDM 2017
Year: 2017

How can we detect fraudulent lockstep behavior in large-scale multi-aspect data (i.e., tensors)? Can we detect it when data are too large to fit in memory or even on a disk? Past studies have shown that dense blocks in real-world tensors (e.g., social media, Wikipedia, TCP dumps, etc.) signal anomalous or fraudulent behavior such as retweet boosting, bot activities, and network attacks. Thus, various approaches, including tensor decomposition and search, have been used for rapid and accurate dense-block detection in tensors. However, all such methods have low accuracy, or assume that tensors are small enough to fit in main memory, which is not true in many real-world applications such as social media and web.
A Warm Welcome Matters!: The Link Between Social Feedback and Weight Loss in /R/Loseit
Authors: Tiago Oliveira Cunha, Ingmar Weber, Gisele L. Pappa
Venue: Conference WWW 2017
Year: 2017

Social feedback has long been recognized as an important element of successful health-related behavior change. How- ever, most of the existing studies look at the effect that offline social feedback has. This paper fills gaps in the literature by proposing a framework to study the causal effect that receiving social support in the form of comments in an online weight loss community has on (i) the probability of the user to return to the forum, and, more importantly, on (ii) the weight loss reported by the user. Using a matching approach for causal inference we observe a difference of 9 lbs lost between users who do or do not receive comments. Surprisingly, this effect is mediated by neither an increase in lifetime in the community nor by an increased activity level of the user. Our results show the importance that a "warm welcome" has when using online support forums to achieve health outcomes.
Equality and social mobility in Twitter discussion groups
Authors: Katherine Ellis, Moisés Goldszmidt, Gert R. G. Lanckriet, Nina Mishra, Omer Reingold
Venue: Conference WSDM 2016
Year: 2016

Online groups, including chat groups and forums, are becoming important avenues for gathering and exchanging information ranging from troubleshooting devices, to sharing experiences, to finding medical information and advice. Thus, issues about the health and stability of these groups are of particular interest to both industry and academia. In this paper we conduct a large scale study with the objectives of first, characterizing essential aspects of the interactions between the participants of such groups and second, characterizing how the nature of these interactions relate to the health of the groups. Specifically, we concentrate on Twitter Discussion Groups (TDGs), self-organized groups that meet on Twitter by agreeing on a hashtag, date and time. These groups have repeated, real-time meetings and are a rising phenomenon on Twitter. We examine the interactions in these groups in terms of the social equality and mobility of the exchange of attention between participants, according to the @mention convention on Twitter. We estimate the health of a group by measuring the retention rate of participants and the change in the number of meetings over time. We find that social equality and mobility are correlated, and that equality and mobility are related to a group's health. In fact, equality and mobility are as predictive of a group's health as some prior characteristics used to predict health of other online groups. Our findings are based on studying 100 thousand sessions of over two thousand discussion groups over the period of June 2012 to June 2013. These finding are not only relevant to stakeholders interested in maintaining these groups, but to researchers and academics interested in understanding the behavior of participants in online discussions. We also find the parallel with findings on the relationship between economic mobility and equality and health indicators in real-world nations striking and thought-provoking.
Measurement and analysis of child pornography trafficking on P2P networks
Authors: Ryan Hurley, Swagatika Prusty, Hamed Soroush, Robert J. Walls, Jeannie R. Albrecht, Emmanuel Cecchet, Brian Neil Levine, Marc Liberatore, Brian Lynn, Janis Wolak
Venue: Conference WWW 2013
Year: 2013

Peer-to-peer networks are the most popular mechanism for the criminal acquisition and distribution of child pornography (CP). In this paper, we examine observations of peers sharing known CP on the eMule and Gnutella networks, which were collected by law enforcement using forensic tools that we developed. We characterize a year's worth of network activity and evaluate different strategies for prioritizing investigators' limited resources. The highest impact research in criminal forensics works within, and is evaluated under, the constraints and goals of investigations. We follow that principle, rather than presenting a set of isolated, exploratory characterizations of users.
Betrayed by Your Dashboard: Discovering Malicious Campaigns via Web Analytics
Authors: Oleksii Starov, Yuchen Zhou, Xiao Zhang, Najmeh Miramirkhani, Nick Nikiforakis
Venue: Conference WWW 2018
Year: 2018

To better understand the demographics of their visitors and their paths through their websites, the vast majority of modern website owners make use of third-party analytics platforms, such as, Google Analytics and ClickTale. Given that all the clients of a third-party analytics platform report to the same server, the tracking requests need to contain identifiers that allow the analytics server to differentiate between their clients. In this paper, we analyze the analytics identifiers utilized by eighteen different third-party analytics platforms and show that these identifiers enable the clustering of seemingly unrelated websites as part of a common third-party analytics account (i.e. websites whose analytics are managed by a single person or team). We focus our attention on malicious websites that also utilize third-party web analytics and show that threat analysts can utilize web analytics to both discover previously unknown malicious pages in a threat-agnostic fashion, as well as to cluster malicious websites into campaigns. We build a system for automatically identifying, isolating, and querying analytics identifiers from malicious pages and use it to discover an additional 11K live domains that use analytics associated with malicious pages. We show how our system can be used to improve the coverage of existing blacklists, discover previously unknown phishing campaigns, identify malicious binaries and Android apps, and even aid in attribution of malicious domains with protected WHOIS information.
Detecting cohesive and 2-mode communities indirected and undirected networks
Authors: Jaewon Yang, Julian J. McAuley, Jure Leskovec
Venue: Conference WSDM 2014
Year: 2014

Networks are a general language for representing relational information among objects. An effective way to model, reason about, and summarize networks, is to discover sets of nodes with common connectivity patterns. Such sets are commonly referred to as network communities. Research on network community detection has predominantly focused on identifying communities of densely connected nodes in undirected networks. In this paper we develop a novel overlapping community detection method that scales to networks of millions of nodes and edges and advances research along two dimensions: the connectivity structure of communities, and the use of edge directedness for community detection. First, we extend traditional definitions of network communities by building on the observation that nodes can be densely interlinked in two different ways: In cohesive communities nodes link to each other, while in 2-mode communities nodes link in a bipartite fashion, where links predominate between the two partitions rather than inside them. Our method successfully detects both 2-mode as well as cohesive communities, that may also overlap or be hierarchically nested. Second, while most existing community detection methods treat directed edges as though they were undirected, our method accounts for edge directions and is able to identify novel and meaningful community structures in both directed and undirected networks, using data from social, biological, and ecological domains.