13 research outputs found

    Detecting collusive spamming activities in community question answering

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    Community Question Answering (CQA) portals provide rich sources of information on a variety of topics. However, the authenticity and quality of questions and answers (Q&As) has proven hard to control. In a troubling direction, the widespread growth of crowdsourcing websites has created a large-scale, potentially difficult-to-detect workforce to manipulate malicious contents in CQA. The crowd workers who join the same crowdsourcing task about promotion campaigns in CQA collusively manipulate deceptive Q&As for promoting a target (product or service). The collusive spamming group can fully control the sentiment of the target. How to utilize the structure and the attributes for detecting manipulated Q&As? How to detect the collusive group and leverage the group information for the detection task? To shed light on these research questions, we propose a unified framework to tackle the challenge of detecting collusive spamming activities of CQA. First, we interpret the questions and answers in CQA as two independent networks. Second, we detect collusive question groups and answer groups from these two networks respectively by measuring the similarity of the contents posted within a short duration. Third, using attributes (individual-level and group-level) and correlations (user-based and content-based), we proposed a combined factor graph model to detect deceptive Q&As simultaneously by combining two independent factor graphs. With a large-scale practical data set, we find that the proposed framework can detect deceptive contents at early stage, and outperforms a number of competitive baselines

    ์†Œ์…œ ๋„คํŠธ์›Œํฌ์™€ ์ด์ปค๋จธ์Šค ํ”Œ๋žซํผ์—์„œ์˜ ์ž ์žฌ ๋„คํŠธ์›Œํฌ ๋งˆ์ด๋‹

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    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ์ปดํ“จํ„ฐ๊ณตํ•™๋ถ€, 2023. 2. ๊ถŒํƒœ๊ฒฝ.์›น ๊ธฐ๋ฐ˜ ์„œ๋น„์Šค์˜ ํญ๋ฐœ์ ์ธ ๋ฐœ๋‹ฌ๋กœ ์‚ฌ์šฉ์ž๋“ค์€ ์˜จ๋ผ์ธ ์ƒ์—์„œ ํญ๋„“๊ฒŒ ์—ฐ๊ฒฐ๋˜๊ณ  ์žˆ๋‹ค. ์˜จ๋ผ์ธ ํ”Œ๋žซํผ ์ƒ์—์„œ, ์‚ฌ์šฉ์ž๋“ค์€ ์„œ๋กœ์—๊ฒŒ ์˜ํ–ฅ์„ ์ฃผ๊ณ ๋ฐ›์œผ๋ฉฐ ์˜์‚ฌ ๊ฒฐ์ •์— ๊ทธ๋“ค์˜ ๊ฒฝํ—˜๊ณผ ์˜๊ฒฌ์„ ๋ฐ˜์˜ํ•˜๋Š” ๊ฒฝํ–ฅ์„ ๋ณด์ธ๋‹ค. ๋ณธ ํ•™์œ„ ๋…ผ๋ฌธ์—์„œ๋Š” ๋Œ€ํ‘œ์ ์ธ ์˜จ๋ผ์ธ ํ”Œ๋žซํผ์ธ ์†Œ์…œ ๋„คํŠธ์›Œํฌ ์„œ๋น„์Šค์™€ ์ด์ปค๋จธ์Šค ํ”Œ๋žซํผ์—์„œ์˜ ์‚ฌ์šฉ์ž ํ–‰๋™์— ๋Œ€ํ•ด ์—ฐ๊ตฌํ•˜์˜€๋‹ค. ์˜จ๋ผ์ธ ํ”Œ๋žซํผ์—์„œ์˜ ์‚ฌ์šฉ์ž ํ–‰๋™์€ ์‚ฌ์šฉ์ž์™€ ํ”Œ๋žซํผ ๊ตฌ์„ฑ ์š”์†Œ ๊ฐ„์˜ ๊ด€๊ณ„๋กœ ํ‘œํ˜„ํ•  ์ˆ˜ ์žˆ๋‹ค. ์‚ฌ์šฉ์ž์˜ ๊ตฌ๋งค๋Š” ์‚ฌ์šฉ์ž์™€ ์ƒํ’ˆ ๊ฐ„์˜ ๊ด€๊ณ„๋กœ, ์‚ฌ์šฉ์ž์˜ ์ฒดํฌ์ธ์€ ์‚ฌ์šฉ์ž์™€ ์žฅ์†Œ ๊ฐ„์˜ ๊ด€๊ณ„๋กœ ๋‚˜ํƒ€๋‚ด์ง„๋‹ค. ์—ฌ๊ธฐ์— ํ–‰๋™์˜ ์‹œ๊ฐ„๊ณผ ๋ ˆ์ดํŒ…, ํƒœ๊ทธ ๋“ฑ์˜ ์ •๋ณด๊ฐ€ ํฌํ•จ๋  ์ˆ˜ ์žˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ๋‘ ํ”Œ๋žซํผ์—์„œ ์ •์˜๋œ ์‚ฌ์šฉ์ž์˜ ํ–‰๋™ ๊ทธ๋ž˜ํ”„์— ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š” ์ž ์žฌ ๋„คํŠธ์›Œํฌ๋ฅผ ํŒŒ์•…ํ•˜๋Š” ์—ฐ๊ตฌ๋ฅผ ์ œ์‹œํ•œ๋‹ค. ์œ„์น˜ ๊ธฐ๋ฐ˜์˜ ์†Œ์…œ ๋„คํŠธ์›Œํฌ ์„œ๋น„์Šค์˜ ๊ฒฝ์šฐ ํŠน์ • ์žฅ์†Œ์— ๋ฐฉ๋ฌธํ•˜๋Š” ์ฒดํฌ์ธ ํ˜•์‹์œผ๋กœ ๋งŽ์€ ํฌ์ŠคํŠธ๊ฐ€ ๋งŒ๋“ค์–ด์ง€๋Š”๋ฐ, ์‚ฌ์šฉ์ž์˜ ์žฅ์†Œ ๋ฐฉ๋ฌธ์€ ์‚ฌ์šฉ์ž ๊ฐ„์— ์‚ฌ์ „์— ์กด์žฌํ•˜๋Š” ์นœ๊ตฌ ๊ด€๊ณ„์— ์˜ํ•ด ์˜ํ–ฅ์„ ํฌ๊ฒŒ ๋ฐ›๋Š”๋‹ค. ์‚ฌ์šฉ์ž ํ™œ๋™ ๋„คํŠธ์›Œํฌ์˜ ์ €๋ณ€์— ์ž ์žฌ๋œ ์‚ฌ์šฉ์ž ๊ฐ„์˜ ๊ด€๊ณ„๋ฅผ ํŒŒ์•…ํ•˜๋Š” ๊ฒƒ์€ ํ™œ๋™ ์˜ˆ์ธก์— ๋„์›€์ด ๋  ์ˆ˜ ์žˆ์œผ๋ฉฐ, ์ด๋ฅผ ์œ„ํ•ด ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ๋น„์ง€๋„ํ•™์Šต ๊ธฐ๋ฐ˜์œผ๋กœ ํ™œ๋™ ๋„คํŠธ์›Œํฌ๋กœ๋ถ€ํ„ฐ ์‚ฌ์šฉ์ž ๊ฐ„ ์‚ฌํšŒ์  ๊ด€๊ณ„๋ฅผ ์ถ”์ถœํ•˜๋Š” ์—ฐ๊ตฌ๋ฅผ ์ œ์•ˆํ•˜์˜€๋‹ค. ๊ธฐ์กด์— ์—ฐ๊ตฌ๋˜์—ˆ๋˜ ๋ฐฉ๋ฒ•๋“ค์€ ๋‘ ์‚ฌ์šฉ์ž๊ฐ€ ๋™์‹œ์— ๋ฐฉ๋ฌธํ•˜๋Š” ํ–‰์œ„์ธ co-visitation์„ ์ค‘์ ์ ์œผ๋กœ ๊ณ ๋ คํ•˜์—ฌ ์‚ฌ์šฉ์ž ๊ฐ„์˜ ๊ด€๊ณ„๋ฅผ ์˜ˆ์ธกํ•˜๊ฑฐ๋‚˜, ๋„คํŠธ์›Œํฌ ์ž„๋ฒ ๋”ฉ ๋˜๋Š” ๊ทธ๋ž˜ํ”„ ์‹ ๊ฒฝ๋ง(GNN)์„ ์‚ฌ์šฉํ•˜์—ฌ ํ‘œํ˜„ ํ•™์Šต์„ ์ˆ˜ํ–‰ํ•˜์˜€๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์ด๋Ÿฌํ•œ ์ ‘๊ทผ ๋ฐฉ์‹์€ ์ฃผ๊ธฐ์ ์ธ ๋ฐฉ๋ฌธ์ด๋‚˜ ์žฅ๊ฑฐ๋ฆฌ ์ด๋™ ๋“ฑ์œผ๋กœ ๋Œ€ํ‘œ๋˜๋Š” ์‚ฌ์šฉ์ž์˜ ํ–‰๋™ ํŒจํ„ด์„ ์ž˜ ํฌ์ฐฉํ•˜์ง€ ๋ชปํ•œ๋‹ค. ํ–‰๋™ ํŒจํ„ด์„ ๋” ์ž˜ ํ•™์Šตํ•˜๊ธฐ ์œ„ํ•ด, ANES๋Š” ์‚ฌ์šฉ์ž ์ปจํ…์ŠคํŠธ ๋‚ด์—์„œ ์‚ฌ์šฉ์ž์™€ ๊ด€์‹ฌ ์ง€์ (POI) ๊ฐ„์˜ ์ธก๋ฉด(Aspect) ์ง€ํ–ฅ ๊ด€๊ณ„๋ฅผ ํ•™์Šตํ•œ๋‹ค. ANES๋Š” User-POI ์ด๋ถ„ ๊ทธ๋ž˜ํ”„์˜ ๊ตฌ์กฐ์—์„œ ์‚ฌ์šฉ์ž์˜ ํ–‰๋™์„ ์—ฌ๋Ÿฌ ๊ฐœ์˜ ์ธก๋ฉด์œผ๋กœ ๋‚˜๋ˆ„๊ณ , ๊ฐ๊ฐ์˜ ๊ด€๊ณ„๋ฅผ ๊ณ ๋ คํ•˜์—ฌ ํ–‰๋™ ํŒจํ„ด์„ ์ถ”์ถœํ•˜๋Š” ์ตœ์ดˆ์˜ ๋น„์ง€๋„ํ•™์Šต ๊ธฐ๋ฐ˜ ์ ‘๊ทผ ๋ฐฉ์‹์ด๋‹ค. ์‹ค์ œ LBSN ๋ฐ์ดํ„ฐ์—์„œ ์ˆ˜ํ–‰๋œ ๊ด‘๋ฒ”์œ„ํ•œ ์‹คํ—˜์—์„œ, ANES๋Š” ๊ธฐ์กด์— ์ œ์•ˆ๋˜์—ˆ๋˜ ๊ธฐ๋ฒ•๋“ค๋ณด๋‹ค ๋†’์€ ์„ฑ๋Šฅ์„ ๋ณด์—ฌ์ค€๋‹ค. ์œ„์น˜ ๊ธฐ๋ฐ˜ ์†Œ์…œ ๋„คํŠธ์›Œํฌ์™€๋Š” ๋‹ค๋ฅด๊ฒŒ, ์ด์ปค๋จธ์Šค์˜ ๋ฆฌ๋ทฐ ์‹œ์Šคํ…œ์—์„œ๋Š” ์‚ฌ์šฉ์ž๋“ค์ด ๋Šฅ๋™์ ์ธ ํŒ”๋กœ์šฐ/ํŒ”๋กœ์ž‰ ๋“ฑ์˜ ํ–‰์œ„๋ฅผ ์ˆ˜ํ–‰ํ•˜์ง€ ์•Š๊ณ ๋„ ํ”Œ๋žซํผ์— ์˜ํ•ด ์„œ๋กœ์˜ ์ •๋ณด๋ฅผ ์ฃผ๊ณ ๋ฐ›๊ณ  ์˜ํ–ฅ๋ ฅ์„ ํ–‰์‚ฌํ•˜๊ฒŒ ๋œ๋‹ค. ์ด์™€ ๊ฐ™์€ ์‚ฌ์šฉ์ž๋“ค์˜ ํ–‰๋™ ํŠน์„ฑ์€ ๋ฆฌ๋ทฐ ์ŠคํŒธ์— ์˜ํ•ด ์‰ฝ๊ฒŒ ์•…์šฉ๋  ์ˆ˜ ์žˆ๋‹ค. ๋ฆฌ๋ทฐ ์ŠคํŒธ์€ ์‹ค์ œ ์‚ฌ์šฉ์ž์˜ ์˜๊ฒฌ์„ ์ˆจ๊ธฐ๊ณ  ํ‰์ ์„ ์กฐ์ž‘ํ•˜์—ฌ ์ž˜๋ชป๋œ ์ •๋ณด๋ฅผ ์ „๋‹ฌํ•˜๋Š” ๋ฐฉ์‹์œผ๋กœ ์ด๋ฃจ์–ด์ง„๋‹ค. ๋‚˜๋Š” ์ด๋ฅผ ํ•ด๊ฒฐํ•˜๊ธฐ ์œ„ํ•ด ์‚ฌ์šฉ์ž ๋ฆฌ๋ทฐ ๋ฐ์ดํ„ฐ์—์„œ ์‚ฌ์šฉ์ž ๊ฐ„ ์‚ฌ์ „ ๊ณต๋ชจ์„ฑ(Collusiveness)์˜ ๊ฐ€๋Šฅ์„ฑ์„ ์ฐพ๊ณ , ์ด๋ฅผ ์ŠคํŒธ ํƒ์ง€์— ํ™œ์šฉํ•œ ๋ฐฉ๋ฒ•์ธ SC-Com์„ ์ œ์•ˆํ•œ๋‹ค. SC-Com์€ ํ–‰๋™์˜ ๊ณต๋ชจ์„ฑ์œผ๋กœ๋ถ€ํ„ฐ ์‚ฌ์šฉ์ž ๊ฐ„ ๊ณต๋ชจ ์ ์ˆ˜๋ฅผ ๊ณ„์‚ฐํ•˜๊ณ  ํ•ด๋‹น ์ ์ˆ˜๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ ์ „์ฒด ์‚ฌ์šฉ์ž๋ฅผ ์œ ์‚ฌํ•œ ์‚ฌ์šฉ์ž๋“ค์˜ ์ปค๋ฎค๋‹ˆํ‹ฐ๋กœ ๋ถ„๋ฅ˜ํ•œ๋‹ค. ๊ทธ ํ›„ ์ŠคํŒธ ์œ ์ €์™€ ์ผ๋ฐ˜ ์œ ์ €๋ฅผ ๊ตฌ๋ณ„ํ•˜๋Š” ๋ฐ์— ์ค‘์š”ํ•œ ๊ทธ๋ž˜ํ”„ ๊ธฐ๋ฐ˜์˜ ํŠน์ง•์„ ์ถ”์ถœํ•˜์—ฌ ๊ฐ๋… ํ•™์Šต ๊ธฐ๋ฐ˜์˜ ๋ถ„๋ฅ˜๊ธฐ์˜ ์ž…๋ ฅ ๋ฐ์ดํ„ฐ๋กœ ํ™œ์šฉํ•˜๋Š” ๋ฐฉ๋ฒ•์„ ์ œ์‹œํ•œ๋‹ค. SC-Com์€ ๊ณต๋ชจ์„ฑ์„ ๊ฐ–๋Š” ์ŠคํŒธ ์œ ์ €์˜ ์ง‘ํ•ฉ์„ ํšจ๊ณผ์ ์œผ๋กœ ํƒ์ง€ํ•œ๋‹ค. ์‹ค์ œ ๋ฐ์ดํ„ฐ์…‹์„ ์ด์šฉํ•œ ์‹คํ—˜์—์„œ, SC-Com์€ ๊ธฐ์กด ๋…ผ๋ฌธ๋“ค ๋Œ€๋น„ ์ŠคํŒธ ํƒ์ง€์— ๋›ฐ์–ด๋‚œ ์„ฑ๋Šฅ์„ ๋ณด์—ฌ์ฃผ์—ˆ๋‹ค. ์œ„ ๋…ผ๋ฌธ์—์„œ ๋‹ค์–‘ํ•œ ๋ฐ์ดํ„ฐ์— ๋Œ€ํ•ด ์—ฐ๊ตฌ๋œ ์•”์‹œ์  ์—ฐ๊ฒฐ๋ง ํƒ์ง€ ๋ชจ๋ธ์€ ๋ ˆ์ด๋ธ”์ด ์—†๋Š” ๋ฐ์ดํ„ฐ์— ๋Œ€ํ•ด์„œ๋„ ์‚ฌ์ „์— ์—ฐ๊ฒฐ๋˜์—ˆ์„ ๊ฐ€๋Šฅ์„ฑ์ด ๋†’์€ ์‚ฌ์šฉ์ž๋“ค์„ ์˜ˆ์ธกํ•˜๋ฏ€๋กœ, ์‹ค์‹œ๊ฐ„ ์œ„์น˜ ๋ฐ์ดํ„ฐ๋‚˜, ์•ฑ ์‚ฌ์šฉ ๋ฐ์ดํ„ฐ ๋“ฑ์˜ ๋‹ค์–‘ํ•œ ๋ฐ์ดํ„ฐ์—์„œ ํ™œ์šฉํ•  ์ˆ˜ ์žˆ๋Š” ์œ ์šฉํ•œ ์ •๋ณด๋ฅผ ์ œ๊ณตํ•˜์—ฌ ๊ด‘๊ณ  ์ถ”์ฒœ ์‹œ์Šคํ…œ์ด๋‚˜, ์•…์„ฑ ์œ ์ € ํƒ์ง€ ๋“ฑ์˜ ๋ถ„์•ผ์—์„œ ๊ธฐ์—ฌํ•  ์ˆ˜ ์žˆ์„ ๊ฒƒ์œผ๋กœ ๊ธฐ๋Œ€ํ•œ๋‹ค.Following the exploding usage on online services, people are connected with each other more broadly and widely. In online platforms, people influence each other, and have tendency to reflect their opinions in decision-making. Social Network Services (SNSs) and E-commerce are typical example of online platforms. User behaviors in online platforms can be defined as relation between user and platform components. A user's purchase is a relationship between a user and a product, and a user's check-in is a relationship between a user and a place. Here, information such as action time, rating, tag, etc. may be included. In many studies, platform user behavior is represented in graph form. At this time, the elements constituting the nodes of the graph are composed of objects such as users and products and places within the platform, and the interaction between the platform elements and the user can be expressed as two nodes being connected. In this study, I present studies to identify potential networks that affect the user's behavior graph defined on the two platforms. In ANES, I focus on representation learning for social link inference based on user trajectory data. While traditional methods predict relations between users by considering hand-crafted features, recent studies first perform representation learning using network/node embedding or graph neural networks (GNNs) for downstream tasks such as node classification and link prediction. However, those approaches fail to capture behavioral patterns of individuals ingrained in periodical visits or long-distance movements. To better learn behavioral patterns, this paper proposes a novel scheme called ANES (Aspect-oriented Network Embedding for Social link inference). ANES learns aspect-oriented relations between users and Point-of-Interests (POIs) within their contexts. ANES is the first approach that extracts the complex behavioral pattern of users from both trajectory data and the structure of User-POI bipartite graphs. Extensive experiments on several real-world datasets show that ANES outperforms state-of-the-art baselines. In contrast to active social networks, people are connected to other users regardless of their intentions in some platforms, such as online shopping websites and restaurant review sites. They do not have any information about each other in advance, and they only have a common point which is that they have visited or have planned to visit same place or purchase a product. Interestingly, users have tendency to be influenced by the review data on their purchase intentions. Unfortunately, this instinct is easily exploited by opinion spammers. In SC-Com, I focus on opinion spam detection in online shopping services. In many cases, my decision-making process is closely related to online reviews. However, there have been threats of opinion spams by hired reviewers increasingly, which aim to mislead potential customers by hiding genuine consumers opinions. Opinion spams should be filed up collectively to falsify true information. Fortunately, I propose the way to spot the possibility to detect them from their collusiveness. In this paper, I propose SC-Com, an optimized collusive community detection framework. It constructs the graph of reviewers from the collusiveness of behavior and divides a graph by communities based on their mutual suspiciousness. After that, I extract community-based and temporal abnormality features which are critical to discriminate spammers from other genuine users. I show that my method detects collusive opinion spam reviewers effectively and precisely from their collective behavioral patterns. In the real-world dataset, my approach showed prominent performance while only considering primary data such as time and ratings. These implicit network inference models studied on various data in this thesis predicts users who are likely to be pre-connected to unlabeled data, so it is expected to contribute to areas such as advertising recommendation systems and malicious user detection by providing useful information.Chapter 1 Introduction 1 Chapter 2 Social link Inference in Location-based check-in data 5 2.1 Background 5 2.2 Related Work 12 2.3 Location-based Social Network Service Data 15 2.4 Aspect-wise Graph Decomposition 18 2.5 Aspect-wise Graph learning 19 2.6 Inferring Social Relation from User Representation 21 2.7 Performance Analysis 23 2.8 Discussion and Implications 26 2.9 Summary 34 Chapter 3 Detecting collusiveness from reviews in Online platforms and its application 35 3.1 Background 35 3.2 Related Work 39 3.3 Online Review Data 43 3.4 Collusive Graph Projection 44 3.5 Reviewer Community Detection 47 3.6 Review Community feature extraction and spammer detection 51 3.7 Performance Analysis 53 3.8 Discussion and Implications 55 3.9 Summary 62 Chapter 4 Conclusion 63๋ฐ•

    Signed Latent Factors for Spamming Activity Detection

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    Due to the increasing trend of performing spamming activities (e.g., Web spam, deceptive reviews, fake followers, etc.) on various online platforms to gain undeserved benefits, spam detection has emerged as a hot research issue. Previous attempts to combat spam mainly employ features related to metadata, user behaviors, or relational ties. These works have made considerable progress in understanding and filtering spamming campaigns. However, this problem remains far from fully solved. Almost all the proposed features focus on a limited number of observed attributes or explainable phenomena, making it difficult for existing methods to achieve further improvement. To broaden the vision about solving the spam problem and address long-standing challenges (class imbalance and graph incompleteness) in the spam detection area, we propose a new attempt of utilizing signed latent factors to filter fraudulent activities. The spam-contaminated relational datasets of multiple online applications in this scenario are interpreted by the unified signed network. Two competitive and highly dissimilar algorithms of latent factors mining (LFM) models are designed based on multi-relational likelihoods estimation (LFM-MRLE) and signed pairwise ranking (LFM-SPR), respectively. We then explore how to apply the mined latent factors to spam detection tasks. Experiments on real-world datasets of different kinds of Web applications (social media and Web forum) indicate that LFM models outperform state-of-the-art baselines in detecting spamming activities. By specifically manipulating experimental data, the effectiveness of our methods in dealing with incomplete and imbalanced challenges is valid

    Using Contextual Features for Online Recruitment Fraud Detection

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    The recent growth of online recruitment and candidate management systems has established yet another media for fraudsters on the internet. The ever-growing size of the candidate pool has forced different industries to move to web-based candidate management systems. The advantages of such web-based systems are substantial. On one hand, they are the best means to filter through thousands of applicants for employers and on the other hand, the candidates find themselves in a convenient position while applying for a position. People with fraudulent motivations explore these systems to lure candidates in a hoax and extract sensitive information (e.g. contact information) using fake job advertisements. In this paper, we analyzed a publicly available dataset and used machine learning algorithms to classify job postings as fraudulent or legitimate. The contribution of this research is the inclusion of contextual features in the feature space, which revealed compelling improvements of accuracy, precision and recall

    Accumulative time-based ranking method to reputation evaluation in information networks

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    With the rapid development of modern technology, the Web has become an important platform for users to make friends and acquire information. However, since information on the Web is over-abundant, information filtering becomes a key task for online users to obtain relevant suggestions. As most Websites can be ranked according to users' rating and preferences, relevance to queries, and recency, how to extract the most relevant item from the over-abundant information is always a key topic for researchers in various fields. In this paper, we adopt tools used to analyze complex networks to evaluate user reputation and item quality. In our proposed accumulative time-based ranking (ATR) algorithm, we incorporate two behavioral weighting factors which are updated when users select or rate items, to reflect the evolution of user reputation and item quality over time. We showed that our algorithm outperforms state-of-the-art ranking algorithms in terms of precision and robustness on empirical datasets from various online retailers and the citation datasets among research publications

    Uncovering Download Fraud Activities in Mobile App Markets

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    Download fraud is a prevalent threat in mobile App markets, where fraudsters manipulate the number of downloads of Apps via various cheating approaches. Purchased fake downloads can mislead recommendation and search algorithms and further lead to bad user experience in App markets. In this paper, we investigate download fraud problem based on a company's App Market, which is one of the most popular Android App markets. We release a honeypot App on the App Market and purchase fake downloads from fraudster agents to track fraud activities in the wild. Based on our interaction with the fraudsters, we categorize download fraud activities into three types according to their intentions: boosting front end downloads, optimizing App search ranking, and enhancing user acquisition&retention rate. For the download fraud aimed at optimizing App search ranking, we select, evaluate, and validate several features in identifying fake downloads based on billions of download data. To get a comprehensive understanding of download fraud, we further gather stances of App marketers, fraudster agencies, and market operators on download fraud. The followed analysis and suggestions shed light on the ways to mitigate download fraud in App markets and other social platforms. To the best of our knowledge, this is the first work that investigates the download fraud problem in mobile App markets.Comment: Published as a conference paper in IEEE/ACM ASONAM 201

    Review manipolation in the hotel industry: the case of one- time contributors

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    Although, review manipulation has shown to have a significant adverse impact on consumer welfare, there is yet little understanding of which economic incentives drive this behavior as most of the current research has focused on the characteristics that define a fake review. The present study investigates these incentives using the innovative approach of examining one-time contributor user reviews as an alternative measure of review manipulation. With a sample comprising 450 hotels, registered on TripAdvisor, from the cities of Amsterdam and Brussels two type of studies were developed encompassing both cross-sectional and panel data analyses. The empirical results obtained show that review manipulation is sufficiently economically important since agents with different economic incentives will indulge in review fraud in a dissimilar extent. These incentives were found to include: the type of organizational structure; the total number of reviews; and the attributed user bubble rating
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