Perceived Frequency of Advertising Practices Sai Teja Peddinti

Allen Collins

Google Inc.

Google Inc.

Google Inc.

[email protected] Nina Taft

[email protected] Anna Turner

[email protected] Allison Woodruff

Google Inc.

Google Inc.

Google Inc.

[email protected]

[email protected]

[email protected]

ABSTRACT

sively on web based activities. Intuitively, this construct may be an explanatory factor for privacy-related attitudes and behaviors. For example, if individuals feel that certain practices occur more frequently, those individuals may have higher levels of concern regarding privacy and/or take more actions to protect their privacy, either in relationship to those specific practices or in general. As a first effort to explore this construct in a concrete way, we have further narrowed the problem space and designed an instrument to measure people’s perceptions of the frequency with which information about individuals is gathered and used in advertising practices. By now, many web users have a general awareness about online advertising practices and tracking technology. However these practices are not the same in all applications, nor for all services, and not for different types of data. As previous work suggests [19], it is quite difficult for an ordinary web user to understand how the advertising ecosystem really works, to know about the various business relationships between the different players, and the implications of different technologies used. Hence, in this study we aim to explore how users perceive this ecosystem. In particular, we seek to understand how frequently participants think various practices do or do not occur, and if they have different perceptions for different web services (such as news, email, shopping, social, photos, etc). We designed a simple metric of people’s perception of the frequency of various ad practices, called Ad Practice Frequency Perception (APFP). We deployed a survey to 1203 members of a representative panel of the US population, and explored whether this metric revealed interesting differentiation across our participants. The survey questions included scenarios in which the stated ad practices were varied across a spectrum from very common to rare or never. Our metric allows us to capture the extent to which users overestimate or underestimate the frequency of a given practice - where users who overestimate believe a practice occurs more frequently than it actually does, and those who underestimate the frequency perceive an ad behavior to be more rare than in practice. Our goal in this initial exploration was not to establish a definitive metric or segmentation, but rather to explore whether this is a promising direction. We found that the participants’ general ability to accurately estimate frequency was fairly poor. Roughly one third underestimate even the common scenarios, while two thirds or more overestimate for practices that are rare. The majority of participants had a fair bit of variance in their answers, implying that they do perceive differences among different scenarios. We used clustering techniques to explore

In this paper, we introduce a new construct for measuring individuals’ privacy-related beliefs and understandings, namely their perception of the frequency with which information about individuals is gathered and used by others for advertising purposes. We introduce a preliminary instrument for measuring this perception, called the Ad Practice Frequency Perception Scale. We report data from a survey using this instrument, as well as the results of an initial clustering of participants based on this data. Our results, while preliminary, suggest that this construct may have future potential to characterize and segment individuals, and is worthy of further exploration.

1.

Aaron Sedley

INTRODUCTION

Characterizing individuals’ privacy-related attitudes and behaviors, and segmenting individuals’ into groups based on these characterizations, are long-sought-after goals, e.g. [3]. Nonetheless, effective measures and segmentations have been elusive. Consider for example Westin’s Privacy Segmentation Index, which is probably the best-known and most often used characterization and segmentation of users according to their privacy-related attitudes [10]: concerns have long existed regarding the assumptions underlying this index and its apparent lack of predictive power, as summarized for example in [22]. Ongoing research in this area follows many arcs. For example, some work seeks to characterize the relationship (or apparent lack thereof) between different measures, e.g. [17, 4, 22]. Other work seeks to produce new constructs and instruments which might yield additional insight and predictive power, e.g. [9, 20]. The work presented in this paper falls in this latter category. Specifically, we propose a new construct for measuring one aspect of people’s privacy-related beliefs and understandings: their perception of the frequency with which information about individuals is gathered and used by others. In this paper, we focus predominantly although not exclu-

Copyright is held by the author/owner. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee. Symposium on Usable Privacy and Security (SOUPS) 2015, July 22–24, 2015, Ottawa, Canada.

1

segmenting the population, and our initial results revealed two groups (one large and one small) whose means were consistently and substantially separated across all questions. The smaller group contains people who more regularly underestimate the actual frequency. We also explored some demographic differences between the two groups. Overall, our findings on underestimation and overestimation show interesting variation in user perceptions. Based on these preliminary findings, we believe perceived frequency warrants further investigation. More work is needed to further segment the larger group since user opinions span a broad range. In the future, this construct might potentially yield an improved ability to characterize and segment users according to their perspective on privacy, particularly in combination with other metrics, although such benefits are as yet speculative.

2.

question, participants were given seven possible answer options based on a 7-point Likert scale (with 1 being the most common, and 7 being the least common). We conducted a round of cognitive testing to refine these questions prior to deploying them. In addition to these nine questions, we gathered participant demographics, such as age and education level, as well as asking questions about use of the Internet and email to identify non-Internet users. The questions were administered to 1203 participants in March 2015 as part of a larger survey on privacy attitudes and behaviors. Participants were members of GfK’s KnowledgePanel, an online panel based on a representative sample of the United States population (although note that the analysis here is done on individual responses and has not been weighted). The question order was randomized for each participant. For easier understanding, we order the questions in Table 1 based on the trends we observed in the results. We removed data from 56 participants who declined to answer one or more questions and from 55 participants who straight-lined by selecting the same option for all the questions, resulting in a sample size of 1092 participants. We asked four experts on industry advertising practices to provide responses to the nine questions. One self-declared as a non-expert on some of the questions, and therefore we exclude that person’s responses from the analyses. There was nearly complete agreement among the remaining three experts, and their responses were at most 1 point different on the Likert scale for all questions. We selected the median expert response as the expert answer in our analysis. For each of the nine questions, we compare each participant’s answer (termed raw answer) to the expert answer. We propose the use of an ad practice frequency perception metric (APFP) that is defined to be the expert answer minus the raw answer. This metric will then vary between -6 and +6. A value of 0 indicates that the participant gave the same answer as the expert, a negative value indicates that the participant underestimated the frequency of the practice, and a positive value indicates an overestimation. To allow some leniency because it is a somewhat subjective scale, we consider that a participant gave a correct answer if the difference is between -1 and +1. Note that while the difference value has a range of +6 to -6, the position of the expert answer on the scale limits the actual allowed range. For example, if the expert answer is 7, the difference values can only range between 0 and +6 (and underestimation is not possible); similarly if the expert answer is 1, then the difference values can range between -6 to 0 (and overestimation is not possible).

METHODOLOGY

Our methodology is inspired by previous research such as McDonald and Cranor’s exploration of people’s attitudes and knowledge regarding behavioral advertising, which included questions about the perceived frequency of two scenarios (online ads based on web search history, and online ads based on email content) [13]. Such previous work was largely concerned with establishing people’s understanding of practices that were actually currently occurring, often with an eye to informing policy, e.g. [18, 13, 20]. Accordingly, the instruments tended to include one or two questions about how common a practice was and/or whether that practice was allowed by law, combined with a number of other questions such as whether participants felt that practice was beneficial or harmful, their technical understanding of that practice, and their practical behavior related to that practice (such as whether or not they cleared cookies or browsing history) [6, 18, 13, 20]. We build upon and extend this previous work by exploring participants’ responses to a larger and wider range of practices, importantly including some that we believe do not currently occur commonly, which allows us to better explore the range of overestimation and underestimation (as opposed to previous work which focused on currently occurring common practices, and therefore naturally primarily explored underestimation). Also in contrast with previous work, we focus explicitly on the use of perceived frequency for characterizing and segmenting users. Our choice of questions was informed by prior research (e.g. [13, 4]), media coverage, internal studies, and our understanding of common industry practices. Based on these, we generated nine questions about the frequency of a range of potential advertising practices. The questions spanned a variety of services and a variety of entities potentially responsible for advertising or tracking (such as ad companies, email services, insurance companies, photo databases, etc.). The specific questions can be found in the Appendix.1 A summary of these questions can be found in Table 1, which highlights the type of service each question focused on, and the potential source of advertising in each scenario. For each

3.

INITIAL OBSERVATIONS

We first looked to see if any of the participants answered all of the questions correctly, counting a raw answer as correct if it was within +/-1 of the expert answer. Interestingly we found that just 1 of our participants answered all nine questions correctly, just 3 participants answered 8 or more questions correctly, 2.7% answered 7 or more correctly, 11.8% answered 6 or more correctly, and only 42.4% answered 5 or more correctly. This suggests that most people have a somewhat limited understanding of the frequency of current advertising practices. In Figure 1 we plot the frequency of responses to the questions as a histogram with our APFP metric on the x-

1 The alert reader may note that AP7, which is inspired by [4], is not specifically about advertising practices, but can readily be extended to these. In hindsight we should have made this revision prior to deployment, and intend to remedy this in future rounds.

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Email

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Shopping

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Data broker

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Ad company

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Online forms

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Social

AP7

Photos

AP8

Medical

AP9

Restaurants

Car manufacturer Marketing company Photo database Insurance company Search engine

Expert Answer

AP8 (medical data) and AP9 (restaurant service/search engine), our participants dramatically overestimate by 4 or 5 Likert points. We might postulate about the source of these misunderstandings: in AP6 it is possible that a fair amount of confusion exists about when such practices do or don’t occur in social networks, and this confusion may be bolstered by what people read in the press [7, 11]; in the case of AP7 people might be exaggerating due to press articles about de-anonymization using photos (such as [15] for example); for AP8 people might not realize that different rules apply to medical data; and in the case of AP9 people might be confused about the interaction between search engines and online forms. Clearly these hypotheses need to be explored in further work. We computed Cronbach’s alpha for participants’ responses to our 9 questions and found it to be 0.79 which indicates an acceptable level of internal reliability for our exploratory research. We now examine the answers on an individual basis, by plotting the mean and standard deviation for each participant in Figure 2. The black line indicates the mean score for each participant over all nine questions, and the top (bottom) of the vertical line indicates the mean plus (minus) one standard deviation (respectively). The significance of the color is explained later in Section 4. The participants are ordered according to increasing mean. First we observe that there are some users at the far left of this plot who always choose answer options 1, 2 or 3. We found that a total of 103 participants, or 9.4%, did this. These people believe that all of our scenarios are common. While they have correct answers for our 4 common scenarios, they have incorrect answers for the 5 scenarios that are neither common nor rare, are rare, or do not occur. These people can be thought of as extreme overestimators as they do not differentiate among the various scenarios and therefore significantly overestimate all of the practices that are rare or do not occur at all. At the other end (far right) we see only 5 participants who always choose options 5, 6,and 7. These participants consistently select answers that correspond to low perceived frequency of all practices. These people may be thought of as extreme underestimators as they seem to believe none of these advertising practices commonly occur. The vast majority of the participants have a broad range of responses, meaning that they select answers across the range from rare to common. This indicates that participants perceive that ad practices vary across different services and applications, though their perception might be influenced by their lack of knowledge about current advertising practices. Figure 2 also shows that the participants’ mean answers vary across a very broad scale, from 1.11 to 6.78, with the majority of the participants’ mean answers spread across the range of 2 to 5. Our survey intentionally included scenarios that range from rare to common, in order to explore both underestimation and overestimation. Overall, we were surprised to see the extent of overestimation. As expected given the nature of the metric, this overestimation occurs predominantly for the scenarios that are more rare.

Very common Very common Somewhat common Somewhat common Neither common nor rare Very rare This never happens This never happens This never happens

Table 1: Ad Practice Question Types axis. We first look at questions AP1 and AP2, for which the expert answer was “Very common” (a value of 2). Recall that 1 is the highest valued answer a participant can give, and that we consider participants to have answered correctly when they are between -1 and +1 of the expert answer. Accordingly, it is not possible by definition for participants to overestimate for these questions. For these very common scenarios, we observe that the majority of participants estimate these questions correctly in that they are aware that these advertising scenarios are quite common. The rest, about 31-37% of the participants, underestimate the frequency of the practices described in these scenarios. These questions focus on scenarios that involve advertising companies, a data broker and an email provider. In questions AP3 and AP4, which relate to scenarios that are “Somewhat common” (a value of 3), we observe that a significantly high fraction of participants answered them correctly (85%). Only 8-10% of participants underestimate the frequency of the practices, and a still smaller percentage overestimate the frequency. Interestingly, both these scenarios relate to news services but the potential advertiser is different in each (data broker and advertising company). For AP5, which relates to the use of contact information entered online being used in telephone marketing, we see that 38% overestimate, 52% get it right, and 10% underestimate. We postulate that users are unaware of where phone call marketing comes from, and may believe that some of it comes from their online activities. The scenarios in questions AP6, AP7, AP8 and AP9 describe practices that generally do not occur today, such as a health insurance company selling an individual’s medical data to an advertising company. We see in the histograms that many participants overestimate the frequency of these scenarios by a substantial amount. 64-95% of participants overestimate these questions, and the amount of overestimation depends upon the question. Even though it is not possible to underestimate for these questions, due to the nature of our scale, we were nonetheless surprised to observe the extent of overestimation. For questions AP6 (social) and AP7 (photo database), the majority of participants significantly overestimate by 2, 3 or 4 Likert scale points; for questions

4.

CLUSTERING

We wanted to evaluate if it was possible to group participants based on their answers to the nine questions, and whether these participant groups exhibited different behav3

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Figure 1: Participant Responses to Ad Practice Questions iors. Each participant is represented as a vector of 9 numbers, where each number corresponds to the APFP metric calculated for a question based on the participant’s answer to the question. These vectors are passed as the input to machine learning clustering techniques. We experimented with different clustering approaches, such as hierarchical clustering [12] and k-means clustering. In hierarchical clustering we use different agglomeration techniques, such as the Ward method [21], Complete linkage [14], Average linkage [14], and Centroid linkage [14] to merge nearby clusters. While the Ward method minimizes the total within-cluster variance when merging, the three linkage methods merge the closest clusters together and differ in how the inter-cluster distance is calculated. For calculating the distance between participant vectors, we use Euclidean distance, Manhattan distance, and correlation distance. We experimented with various ways of combining the clustering techniques, 3 distance metrics, and 4 agglomeration methods, and varied the number of clusters from 2 to 10. For comparing the performance of different clustering outputs, we relied on the inherent cluster properties such as the Dunn index [5], Silhoutte width [16], and connectivity [8]. These properties measure the cluster homogeneity (using intra-cluster variance), degree of separation between clusters, and connectedness within a cluster. All the ex-

perimentation was done using the R statistical tool and its packages – clValid [1] and hclust [2]. Based on the three cluster properties, hierarchical clustering using the complete linkage method and Euclidean distance yielded the best results. The best grouping suggests organizing the participants into 2 clusters. The visually interpretable output of the hierarchical clustering is shown in Figure 3. We can clearly see the existence of two user groups (one small with 96 participants and the other large with 996 participants). Visually, we do see the possibility of having more than 2 clusters, however our initial exploration did not result in any clear division among participants in the larger cluster. In future, we plan to explore further if it is possible to regroup the users in the larger cluster into multiple smaller clusters that may exhibit clearly distinct properties.

4.1

Cluster Properties

We observed significant differences among the participants in the two clusters. The scatter plot in Figure 4 indicates the distribution of participants in the two clusters based on their APFP scores computed across the 9 questions. Each point represents an individual participant, where the shape and color of the point are chosen based on the cluster. The points have been jittered (a little random noise is added to the data) in order to see the participant cloud more clearly. The lines indicate the mean ad practice frequency percep4

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Figure 2: Participants’ Raw Answers - Mean and Standard Deviation. Color Corresponds to the Assigned Cluster.

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AP9

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than those in Cluster 1, especially for common ad practices. Figure 5 indicates that for questions AP3, AP4, AP6, and AP9, there is less overlap between the standard deviation bars across the two participant clusters and that there is consistent difference between the means. This implies that these four questions are possible differentiators of participants across the clusters. It appears that what our clustering algorithm is doing is segmenting out the underestimaters (Cluster 2) largely based upon their answers to questions AP3 and AP4, which represent somewhat common practices that these participants may not be aware of. Also it puts the overestimaters in Cluster 1 largely based upon their exaggerated perception of the frequency of scenarios AP6 and AP9. Figure 2 shows the participants in the two clusters using different colors. We see a fairly good demarcation between the participants in the two clusters, where the participants in the smaller cluster (Cluster 2) tend to be concentrated near the top right. This also reiterates that the participants in the smaller cluster often tend to underestimate, with their individual means lying around the ‘Somewhat rare’ and ‘Very rare’ options.

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1195 525 1000 1042 755 869 248 166 609 218 241 106 336 1071 1045 1017 505 1075 1082 991 344 456 307 997 28 1280 1106 1200 1088 1110 242 1186 500 110 317 818 306 200 840 1217 16 989 296 972 153 992 1061 976 626 687 58 135 955 1161 1273 1123 1040 301 38 1255 416 930 685 464 984 674 968 215 149 719 137 167 1279 1276 340 881 725 21 1270 208 180 78 1192 370 988 530 931 22 88 1260 1124 532 599 959 1175 1140 658 773 600 469 192 918 759 1077 872 472 804 1141 445 857 551 486 1225 267 3 965 1242 356 971 311 810 1073 828 732 1081 229 524 858 1003 1039 62 109 710 33 270 121 742 1247 606 178 867 702 1244 297 103 258 168 681 1027 949 714 1268 1113 394 696 462 79 236 11 1191 84 145 746 144 497 772 333 198 495 589 926 238 424 682 279 945 1060 769 592 637 690 193 80 1230 454 830 779 667 1094 927 332 1074 816 1269 788 1107 1227 24 1111 158 1202 1206 743 820 625 556 844 935 624 953 353 1179 1019 1120 1144 537 812 461 37 100 334 349 526 291 1048 43 380 314 838 179 140 1015 129 977 886 715 1252 396 449 1188 508 795 359 1055 1038 850 407 175 1126 1193 260 706 936 813 1205 699 541 958 891 712 133 1238 651 907 870 632 111 587 1207 1037 351 441 529 421 880 898 41 283 408 1245 831 1025 387 116 163 1157 6 214 244 644 603 669 1145 979 74 444 774 89 171 114 94 379 1241 519 1233 1105 470 212 550 447 161 836 1235 134 1010 663 941 246 1100 487 467 1150 510 98 1130 338 489 57 9 924 474 801 502 355 253 1070 756 450 397 683 854 120 610 614 230 108 770 859 868 513 222 748 184 107 1051 990 974 1259 459 119 83 81 882 576 1032 707 653 95 1218 966 27 790 834 1246 216 29 823 912 800 899 528 1128 275 814 1004 414 255 906 479 290 206 325 1014 1251 386 717 621 1194 1012 54 1050 860 843 827 264 376 1158 390 730 1066 523 1131 20 1184 4 908 1102 259 711 900 1016 254 118 293 150 1134 797 636 783 947 705 757 460 666 849 282 957 1146 775 1036 825 1199 929 205 324 893 201 1264 50 650 1024 876 61 1162 1021 157 360 406 366 522 1180 399 1023 1068 917 365 289 1139 237 368 903 10 542 1149 1078 326 1237 393 509 928 1277 708 574 478 64 580 1189 786 1155 49 182 136 960 112 726 377 335 1236 672 724 723 583 753 1076 776 395 782 1249 438 186 1215 787 439 1132 694 1063 59 1234 921 181 411 113 1064 700 475 798 304 649 391 1006 692 1054 861 1142 5 173 659 1089 985 388 889 703 546 693 735 590 598 975 829 853 162 235 1274 978 737 316 299 1118 1034 642 431 668 593 1248 944 457 346 874 210 452 463 442 372 933 777 851 138 533 141 767 419 657 436 1125 172 994 534 809 233 348 1112 559 473 747 619 1030 660 124 331 952 191 177 998 803 322 855 373 152 999 762 794 1152 741 347 1216 535 13 26 115 207 76 209 148 784 983 586 512 897 950 481 662 1167 389 506 187 493 815 190 1001 654 25 1115 940 99 594 567 428 90 901 749 281 516 664 189 97 964 671 920 1211 147 482 1271 14 278 126 1101 1201 1219 1133 811 330 536 675 1173 427 521 832 405 77 453 92 617 676 400 430 514 63 1187 613 553 240 1176 1258 1160 925 225 628 60 1136 251 507 104 435 568 938 1095 720 378 993 323 1026 545 67 271 1137 863 422 887 1261 1052 582 40 895 1046 117 231 910 418 597 875 1181 1256 970 123 312 701 17 337 688 458 35 1098 285 420 515 785 630 102 643 1009 34 738 833 265 1049 31 87 1148 684 402 1056 888 274 194 954 1035 105 640 257 286 750 1154 280 1163 494 211 226 733 842 839 1197 543 69 1121 679 440 32 47 909 91 806 174 1223 511 639 329 476 571 601 799 227 905 585 503 961 277 736 1092 383 943 443 1196 245 19 101 1212 673 339 623 678 879 55 791 677 1275 1062 1204 466 1067 807 538 570 1257 638 520 1047 65 1166 866 579 1097 608 956 1143 1229 704 219 223 1043 1058 1171 751 1164 1159 165 841 919 1041 612 913 573 85 488 627 539 547 52 204 1127 562 232 309 914 437 342 1044 164 71 778 1103 845 374 554 471 602 1022 996 197 691 1172 412 620 962 1065 51 884 987 42 362 46 680 1147 792 1129 821 203 468 611 308 1096 23 904 477 345 1198 633 618 455 768 82 846 661 401 758 410 156 963 566 239 822 713 127 877 951 588 1190 549 213 1109 1281 740 745 1002 1220 310 1013 878 848 939 70 697 847 709 18 986 128 1005 835 433 423 946 243 1266 1203 1170 256 634 560 1174 404 517 160 496 234 185 249 754 686 1224 195 122 176 300 343 647 7 631 531 552 873 364 484 66 883 448 856 591 199 819 68 352 262 629 1177 1116 596 305 764 15 139 948 371 902 268 622 1278 221 354 923 367 564 295 269 648 288 369 689 1008 1228 315 1085 1053 615 169 247 170 328 982 1226 426 1086 1080 604 1165 1267 548 385 403 915 8 321 361 967 93 561 734 273 1182 341 635 718 159 188 39 916 1214 793 357 217 577 492 350 1250 132 1209 53 540 130 670 375 446 763 303 1031 665 969 319 146 155 284 557 1033

0

AP3

Figure 5: Raw Answers - Mean and Standard Deviation across Clusters

5

Height

AP2

Users Figure 3: Hierarchical Cluster Dendrogram

4.2

tion values computed for each question across participants in each cluster. As we can see from Figure 4, the mean APFP values for the questions across participants in the two clusters are consistently and substantially separated. The gap between the means across the two clusters varies between 1.0 to 2.36 depending upon the question. The overall mean for Cluster 2 is 4.84 (which rounds to the answer ‘Somewhat rare’), whereas for Cluster 1 it is 3.16 (which rounds to the answer ‘Somewhat common’). Thus the average difference is 1.68 Likert scale points (on a 7-point scale) between the average participants in the two clusters, a fairly substantial difference which indicates the two clusters do indeed separate participants. This difference between the clusters is also visible in Figure 5, where we show the mean (dots) as well as the mean plus (minus) one standard deviation (top and bottom bar lines, respectively) for the raw answers computed across participants within a cluster. Based on both of these results, we can state that Cluster 2 participants underestimate more

Differences in Participant Demographics

To explore if there are any characteristic differences among participants in the two clusters and to see if we can infer some probable causes for the differences in their responses, we studied the demographic details of the participants. We analyzed the responses of the participants (across the two clusters) to four demographic questions. We noticed no major differences in whether the participants were nonInternet users: 94% of participants in the smaller cluster (Cluster 2) and 97% of participants in the larger cluster (Cluster 1) occasionally used either email or the Internet. Accordingly, Internet usage does not seem to be the primary factor causing the difference in responses across the clusters. Participants over 50 years old constitute 67% of the smaller cluster and 57% of the larger cluster. So, the fraction of people over 50 years is 10% more in Cluster 2 than Cluster 1. (All participants were over the age of 18, thus shifting up the average age compared with the general US population.) We also observed that the smaller cluster has 5

6



Ad Practice Frequency Perception Metric

2

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−6 AP1

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Ad Practice Questions

Figure 4: Participant Clusters and their Means 10% fewer participants holding a Bachelors or higher degree: 26% in the smaller cluster compared to 36% in the larger cluster. Therefore, Cluster 2 has somewhat older and less educated participants. This may partially explain why Cluster 2 participants underestimate more, although these demographic trends do not appear to have large influence.

5.

social networking (AP6) and the interaction between search engines and online restaurant forms (AP9). The bulk of the participants in the smaller cluster are underestimators. In examining the demographics of these two groups, we found that these underestimators are somewhat older and somewhat less educated than the people in the larger cluster. Further segmentation of the population in the larger cluster turned out to be challenging as the remaining 91.2% of our participants’ opinions span a broad range and they were not immediately separable into consistent sub-groups. Our initial findings in our hierarchical clustering dendrogram indicate that it might be fruitful to explore segmenting the population into as many as 4 to 6 sub-groups. We plan to explore this further in our future work. Overall, these results suggest that our new construct, based on the perceived frequency of ad practices, together with our preliminary instrument, can reveal people’s varying perceptions of the frequency of advertising practices. Our findings on underestimation and overestimation show interesting variation in participants’ perceptions. To improve segmentation of the population, we plan to study refining our clusters and making them more granular. We will explore further subdividing our larger cluster into smaller segments by analyzing subsets of questions. We intend to revisit and refine the few survey questions where our experts were not in complete agreement. Importantly, we plan to explore the connection between the perception of online advertising practices with privacy attitudes and behaviors (available from other parts of the larger survey). It is also important to understand how the public’s understanding and awareness of these practices evolve over time.

CONCLUSIONS AND FUTURE WORK

In this paper, we reported the results of an initial exploration of the perceived frequency of various advertising practices, such as those related to email activity, web browsing behavior (search, shopping and news), remarketing, online forms and social networks. We also included scenarios which are rare, or don’t happen at all, to examine the limits of people’s understanding. Overall participants’ ability to accurately estimate which ad practices are frequent or infrequent was fairly poor: only 42% were able to get 5 or more questions right; roughly one third underestimate common scenarios; and anywhere between 64-95% (depending upon the scenario) overestimate scenarios that occur rarely or never. Future work is needed to understand this inaccurate sense of the frequency of advertising business practices. At the same time, participants did vary their answers across our scenarios, and did appear to realize that multiple practices do exist. We explored a number of clustering methods to see if our population can be naturally segmented into groups of people with similar perceptions. Our best clustering method was able to create two groups with one small cluster (8.8% of participants) clearly distinct from the rest. The difference in average answers between these clusters was 1.68 points on a 7-point Likert scale. We observed that a key difference between these two groups of people lies in their answers to scenarios about news (AP3, AP4),

6. 6

ACKNOWLEDGMENTS

We are grateful to Marianne Berkovich and Martin Ortlieb for developing initial versions of three of the scenarios. Our thanks to Mario Callegaro, Sunny Consolvo, Rick Malins, and Lawrence You for helpful advice, input, and support.

7.

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REFERENCES

[1] clValid: Validation of Clustering Results. http://cran.r-project.org/web/packages/ clValid/index.html. [2] R: Hierarchical Clustering. https://stat.ethz.ch/R-manual/R-patched/ library/stats/html/hclust.html. [3] Workshop on Privacy Personas and Segmentation (PPS). In Symposium on Usable Privacy and Security (SOUPS 2014), 2014. [4] A. Acquisti, R. Gross, and F. Stutzman. Face recognition and privacy in the age of augmented reality. Journal of Privacy and Confidentiality, 6(2):1, 2014. [5] J. C. Dunn. Well-separated clusters and optimal fuzzy partitions. Journal of Cybernetics, 4(1), 1974. [6] S. Fox. Trust and privacy online: Why Americans want to rewrite the rules. In Pew Internet & American Life Project, 2000. [7] V. Goel. How Facebook Sold You Krill Oil. http://www.nytimes.com/2014/08/03/technology/ how-facebook-sold-you-krill-oil.html. [8] J. Handl, J. Knowles, and D. B. Kell. Computational cluster validation in post-genomic data analysis. Bioinformatics, 21(15), 2005. [9] B. P. Knijnenburg. Information disclosure profiles for segmentation and recommendation. In Symposium on Usable Privacy and Security (SOUPS 2014) Workshop on Privacy Personas and Segmentation (PPS), 2014. [10] P. Kumaraguru and L. F. Cranor. Privacy indexes: A survey of Westin’s studies. ISRI Technical Report, 2005. [11] L. Kwoh. Beware: Potential Employers Are Watching You. http://www.wsj.com/articles/ SB10000872396390443759504577631410093879278. [12] C. D. Manning, P. Raghavan, and H. Sch¨ utze. Hierarchical clustering. In Introduction to Information Retrieval, 2008. [13] A. McDonald and L. F. Cranor. Beliefs and behaviors: Internet users’ understanding of behavioral advertising. In 38th Research Conference on Communication, Information and Internet Policy (Telecommunications Policy Research Conference), 2010. [14] F. Murtagh. A survey of recent advances in hierarchical clustering algorithms. The Computer Journal, 26(4), 1983. [15] J. Risen and L. Poitras. N.S.A. Collecting Millions of Faces From Web Images. http://www.nytimes.com/2014/06/01/us/ nsa-collecting-millions-of-faces-from-web-images. html. [16] P. Rousseeuw. Silhouettes: A graphical aid to the interpretation and validation of cluster analysis. J. Comput. Appl. Math., 20(1), 1987. [17] S. Spiekermann, J. Grossklags, and B. Berendt. E-privacy in 2nd generation E-commerce: Privacy 7

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preferences versus actual behavior. In Proceedings of the 3rd ACM conference on Electronic Commerce, 2001. J. Turow. Americans & online privacy: The system is broken. Annenberg Public Policy Center, University of Pennsylvania, 2003. B. Ur, P. G. Leon, L. F. Cranor, R. Shay, and Y. Wang. Smart, useful, scary, creepy: Perceptions of online behavioral advertising. In Proceedings of the Eighth Symposium on Usable Privacy and Security, SOUPS ’12, pages 4:1–4:15, New York, NY, USA, 2012. ACM. J. M. Urban and C. J. Hoofnagle. The privacy pragmatic as privacy vulnerable. In Symposium on Usable Privacy and Security (SOUPS 2014) Workshop on Privacy Personas and Segmentation (PPS), 2014. J. H. Ward. Hierarchical grouping to optimize an objective function. Journal of the American Statistical Association, 58, 1963. A. Woodruff, V. Pihur, S. Consolvo, L. Schmidt, L. Brandimarte, and A. Acquisti. Would a privacy fundamentalist sell their DNA for $1000 ... if nothing bad happened as a result? The Westin categories, behavioral intentions, and consequences. In 10th Symposium on Usable Privacy and Security (SOUPS), 2014.

APPENDIX A.

AD PRACTICE FREQUENCY PERCEPTION QUESTIONS

For all of our questions, the participants were given 7 possible answers to choose from. We repeat these only once here at the beginning as they are the same for all nine questions. 1. This happens every time 2. Very common 3. Somewhat common 4. Neither common nor rare 5. Somewhat rare 6. Very rare 7. This never happens AP1. Suppose an Internet user is reading their email online, and their email provider displays ads to them. In that situation, how common is the following: The ads are based on the content of email the user sends and receives. AP2. Suppose an Internet user places a pair of shoes in a shopping cart at a retail website, but doesn’t proceed to purchase them. In that situation, how common is the following: An ad company associated with the retail website plus other websites tracks this information, and shows the user ads for the same shoes when they visit other websites. AP3. Suppose an Internet user visits the website for a major newspaper and sees an ad for a hotel chain. In that situation, how common is the following: The ad for the hotel chain does not come to the user directly from the hotel chain. Instead, the newspaper buys information from other companies that track and collect information

about which websites individual users visit, and the newspaper uses this information to determine which ads to show to each user. AP4. Suppose an Internet user visits the website for a major newspaper and sees an ad for an airline. In that situation, how common is the following: The ad for the airline does not come to the user directly from the airline. Instead, an ad company determines which ads to show to the user, personally, based on the history of websites they have visited previously. AP5. Suppose an Internet user is in the early stages of car shopping and visits websites for several car companies. During their exploration, they register at the website of a major car manufacturer and configure a car. In that situation, how common is the following: Based on the contact information the user enters at the website, the local car dealership for that manufacturer phones the user to ask if they are interested in test driving a car. AP6. Suppose an Internet user publicly posts pictures from a recent vacation on a major social network. In that situation, how common is the following: A marketing company scans the content of the social network and sells information about the user’s interests to other companies, including travel agencies. Later that week they get an email message from an airline company about getaways to similar vacation locations. AP7. Suppose a person is walking down the street. In that situation, how common is the following: A stranger snaps a photo of that person and uses an app on their phone to get information about them. By using online photos and databases, the app determines their name, home address, age, bank account balance, and travel history, and displays that information to the stranger. AP8. Suppose an Internet user wants to apply for health insurance and fills out an application form on the website of a major medical insurance company, including the fact that they have recently been diagnosed with diabetes. In that situation, how common is the following: The medical insurance company sells this information to an online ad company, and the Internet user starts seeing ads about diabetes medication on websites as they surf the Internet. AP9. Suppose an Internet user searches for restaurant reservations on a search engine, and then clicks through to a restaurant reservation website that is not affiliated with the search engine. In that situation, how common is the following: The search engine tracks the information the user enters at the restaurant reservation website, and uses it to customize ads the next time they use the search engine.

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