Using Service-Learning in Graduate Curriculum to Address Teenagers\u27 Vulnerability to Web Misinformation

We report on how we implemented service-learning (S-L) in a CS graduate class to improve student understanding of the class materials and provide a service to the community, i.e., addressing teenagers’ vulnerability to Web misinformation. We show how S-L benefits CS students in their course theory understanding and personal skills development, while teenagers’ news media literacy and misinformation detection accuracy were positively impacted

VEWS: A Wikipedia Vandal Early Warning System

We study the problem of detecting vandals on Wikipedia before any human or known vandalism detection system reports flagging potential vandals so that such users can be presented early to Wikipedia administrators. We leverage multiple classical ML approaches, but develop 3 novel sets of features. Our Wikipedia Vandal Behavior (WVB) approach uses a novel set of user editing patterns as features to classify some users as vandals. Our Wikipedia Transition Probability Matrix (WTPM) approach uses a set of features derived from a transition probability matrix and then reduces it via a neural net auto-encoder to classify some users as vandals. The VEWS approach merges the previous two approaches. Without using any information (e.g. reverts) provided by other users, these algorithms each have over 85% classification accuracy. Moreover, when temporal recency is considered, accuracy goes to almost 90%. We carry out detailed experiments on a new data set we have created consisting of about 33K Wikipedia users (including both a black list and a white list of editors) and containing 770K edits. We describe specific behaviors that distinguish between vandals and non-vandals. We show that VEWS beats ClueBot NG and STiki, the best known algorithms today for vandalism detection. Moreover, VEWS detects far more vandals than ClueBot NG and on average, detects them 2.39 edits before ClueBot NG when both detect the vandal. However, we show that the combination of VEWS and ClueBot NG can give a fully automated vandal early warning system with even higher accuracy.Comment: To appear in Proceedings of the 21st ACM SIGKDD Conference of Knowledge Discovery and Data Mining (KDD 2015

An Analysis of People’s Reasoning for Sharing Real and Fake News

The problem of the increase in the volume of fake news and its widespread over social media has gained massive attention as most of the population seeks social media for daily news diet. Humans are equally responsible for the surge of fake news spread. Thus, it is imperative to understand people’s behavior when they decide to share real and fake news items on social media. In an attempt to do so, we performed an analysis on data collected through a survey where participants (n= 363) were asked whether they were willing to share the given news item on their social media and explain the reasoning for their decision. The results show that the analysis presents several commonalities with previous studies. Moreover, we also addressed the problem of predicting whether a person will share a given news item or not. For this, we used intrinsic features from participants’ open-ended responses and demographics attributes. We found that the perceived emotions triggered by the news item show a strong influence on the user’s decision to share news items on social media

On the Termination of Logic Programs with Function Symbols

Recently there has been an increasing interest in the bottom-up evaluation of the semantics of logic programs with complex terms. The main problem due to the presence of functional symbols in the head of rules is that the corresponding ground program could be infinite and that finiteness of models and termination of the evaluation procedure is not guaranteed. This paper introduces, by deeply analyzing program structure, new decidable criteria, called safety and Gamma-acyclicity, for checking termination of logic programs with function symbols under bottom-up evaluation. These criteria guarantee that stable models are finite and computable, as it is possible to generate a finitely ground program equivalent to the source program. We compare new criteria with other decidable criteria known in the literature and show that the Gamma-acyclicity criterion is the most general one. We also discuss its application in answering bound queries

Checking termination of bottom-up evaluation of logic programs with function symbols

Recently, there has been an increasing interest in the bottom-up evaluation of the semantics of logic programs with complex terms. The presence of function symbols in the program may render the ground instantiation infinite, and finiteness of models and termination of the evaluation procedure, in the general case, are not guaranteed anymore. Since the program termination problem is undecidable in the general case, several decidable criteria (called program termination criteria) have been recently proposed. However, current conditions are not able to identify even simple programs, whose bottom-up execution always terminates. The paper introduces new decidable criteria for checking termination of logic programs with function symbols under bottom-up evaluation, by deeply analyzing the program structure. First, we analyze the propagation of complex terms among arguments by means of the extended version of the argument graph called propagation graph. The resulting criterion, called Gamma-acyclicity, generalizes most of the decidable criteria proposed so far. Next, we study how rules may activate each other and define a more powerful criterion, called safety. This criterion uses the so-called safety function able to analyze how rules may activate each other and how the presence of some arguments in a rule limits its activation. We also study the application of the proposed criteria to bound queries and show that the safety criterion is well-suited to identify relevant classes of programs and bound queries. Finally, we propose a hierarchy of classes of terminating programs, called k-safety, where the k-safe class strictly includes the (k-1)-safe class. Note: To appear in Theory and Practice of Logic Programming (TPLP).Comment: 36 pages, 7 figure

Ensuring the Integrity of Wikipedia: A Data Science Approach

In this paper, we present our research on the problem of ensuring the integrity of Wikipedia, the world\u27s biggest free encyclopedia. As anyone can edit Wikipedia, many malicious users take advantage of this situation to make edits that compromise pages\u27 content quality. Specifically, we present DePP, the state-of-the-art tool that detects article pages to protect with an accuracy of 93% and we introduce our research on identifying spam users. We show that we are able to classify spammers from benign users with 80.8% of accuracy and 0.88 mean average precision

Modeling Misinformation Diffusion in Social Media: Beyond Network Properties

In this paper, we discuss the current limitations of existing models for misinformation diffusion in social media and present our current work suggesting that other factors beyond network properties play an important node in modeling misinformation spread and profiling fake news spreaders. These factors include news and user characteristics such as user demographics, profile properties, and behavior and activity, and news style and content complexity

SQUID

Graph databases such as chemical databases, protein databases, and RNA motif databases, are simply a collection of graphs. Querying a graph database involves the computation of a subgraph isomorphism problem (which is NP-complete) for each graph in the database. Therefore, an index is required to filter out false positives and reduce the number of subgraph isomorphisms to compute. In this demo, we introduce SQUID, a scalable system for querying, updating and indexing dynamic graph databases, i.e., databases changing over time, and showcase it on chemical databases. The tool uses a graph coarsening-based index that is able to answer both subgraph and supergraph queries. It also allows the database to be changed with an automatic index update. Also, it displays information found in the graph database in a concise manner that is easier to understand

A Scalable Graph-Coarsening Based Index for Dynamic Graph Databases

A graph database D is a collection of graphs. To speed up subgraph query answering on graph databases, indexes are commonly used. State-of-the-art graph database indexes do not adapt or scale well to dynamic graph database use; they are static, and their ability to prune possible search responses to meet user needs worsens over time as databases change and grow. Users can re-mine indexes to gain some improvement, but it is time consuming. Users must also tune numerous parameters on an ongoing basis to optimize performance and can inadvertently worsen the query response time if they do not choose parameters wisely. Recently, a one-pass algorithm has been developed to enhance the performance of frequent subgraphs based indexes by using the algorithm to update them regularly. However, there are some drawbacks, most notably the need to make updates as the query workload changes. In this paper, we propose a new index based on graph-coarsening to speed up subgraph query answering time in dynamic graph databases. Our index is parameter-free, query-independent, scalable,small enough to store in the main memory, and is simpler and less costly to maintain for database updates. Experimental results show that our index outperforms hybrid-indexes (i.e. indexes updated with one-pass) for query answering time in the case of social network databases, and is comparable with these indexes for frequent and infrequent queries on chemical databases. Our index can be updated up to 60 times faster in comparison to one-pass on dynamic graph databases. Moreover, our index is independent of the query workload for index update and is up to 15 times faster after hybrid-indexes are attuned to query workload