Index
ActiveAgent spambot, 174
Active Threat Level Analysis System (ATLAS), 188
Address list generation, 174, 183
Python-Dev mailing list, 139–140
“target lists,” 176, 178
Adler, James, 108
Advance-fee fraud messages, 102, 180
“Aesthetics of access” (Hilderbrand), 73
African continent, 186
Air Force Cyber Command, 192
Algorithms, computer programming (see Bayesian filter; PageRank algorithm)
intersecting human labor, 166
IRC protocol, 173
predominance of in spam/antispam struggles, 63, 150, 166
AltaVista search engine, 117
alt.spam, 216n78
Amazon.com, 166
Anarchists, 11, 15, 33, 39, 40, 55
anarchistic charivari, 46, 94
“Anatomy of a Large-Scale Hypertextual Web Search Engine,” 120–121
Anderson, C. W., 162, 165–166
Andreessen, Marc, 53
“anon.penet.fi” remailing, 40
Antigate, 170–171
Antimalware kits, 183–184
Antispam initiatives, early. 34, 43, 45. See also Charivari; Vigilantism; by name, e.g., Spam filters
abuse of early spammers, 64–66
battle on Usenet, 67, 71–74
charivari, the, 34, 43–47, 54, 56, 57, 73, 81, 94, 131, 174, 192, 203
flaming, 43, 45, 46, 56, 81, 84
government actions (see FBI; FTC)
“JJ” letter, 41–43
market “solutions,” 99
picketing companies, 67
“scam baiting,” 102, 216n91
shaming, 43, 44, 46, 73, 81, 84
Antispam legislation, 66, 101
California Business & Professions Code §17529.5, 66
CAN-SPAM Act, U.S., xx, 66, 73, 93, 98–99, 100, 118, 142
effective combined with filtering, 143–144
HR 1748, 66, 91, 92
nebulous laws and jurisdictional confusion, 70
Antispam players, 66. See also NANAE
alt.spam, 216n78
corporate market for products, 191
jurisdictional issues, 193–194
military and security important, 185–186, 190
Antispam/spam war, struggles of algorithms, 63, 150, 166
Antispam techniques and methods. See also by name, e.g., Spam filters
human/manual deselection, 137, 146
page ranking (see PageRank algorithm; Reputation factor)
reducing profitability 133–135, 141–142, 154 (see also Bayesian filters; Graham, Paul)
Antivirus software, 89, 181, 186, 188, 191
AOL (America Online). See also ISPs (Internet Service Providers)
generating articles to frame ads, 164–165
intranetwork emailing, 75–76
walled-garden approach, 52, 164
API (Application Programming Interface), 159
Arbor Networks, 188
ARPA (Advanced Research Projects Agency), 4–5, 17–18
ARPANET, 5, 7, 9–12, 19, 27, 33, 35, 36, 37, 38, 42, 48, 49, 50–51
features of the system, 22–24
MSGGROUP list, 25–26, 30
rules, 29–30
wizards of, 22, 23
ARPANET News, 23–24
“@gag” command, 16–17
Atemporality, 151–152
Attention. See also Salience
developing media platforms that respect, 204
Internet as a medium of, xiv, 162, 199
regulation and attention epoch (2002–present), xxi
spam as strategies for wasteful allocation of, 112, 201–204
Atwood, Jeff, 114
Auto-erasing systems, 40–41, 127
Automated text, 41, 159, 161–162. See also Litspam; Spambots; Splogs/splogging
content farms, 155, 162–164, 166, 197
preprogrammed, 174
Automation, 84, 135, 200. See also Human-machine distinction; Infrastructure of information technology
Avalanche software, 66
“Backbone Cabal,” 40
Baidu search engine (China), 114
Banco Noroeste, 108
Baran, Paul, 22
Bateson, Gregory, 50
Bay Area, California, early networks in, 3–4
Bayesian filters, 141, 143, 192
destroying spam as a reputable business model, 143–144
determining probability of spam, 136–137
false positives and, 137–138, 145
naïve Bayesian variants (Graham), 129, 135–139, 140, 155, 220nn19–20
Bayesian probability, 135
Bayes, Thomas, 135–136
BBSes (Bulletin Board Systems), 3
Bell Labs, 5
Benkler, Yochai, 26, 58, 202
Berkeley Community Memory, 2
Berners-Lee, Timothy, 53, 116
Betterly, Laura, 66, 69
“Blocklist” projects, 66
Blogger, Pyra Labs, 158–159
Blogging. See Splogs/splogging
Bookmarking sites, payoff systems, 167
Boredom, 123–124. See also Attention
Borges, Jorge Luis, 146
Borgesian publishing economics, 127, 183
“The Library of Babel,” 146, 180, 183, 219n3
Bos, Peter, 28–29, 60, 194
Bot machines, 173–174, 176–177
Botmasters, xii, xiii, 175–176, 181
market for and profitability, 175, 177–180
Botnets, xiii, 144, 169, 171–175, 186–187, 192–193, 196, 199. See also Malware; Spambots; Storm; Worms; by name
alliance of spam and malware, 191
communicating among themselves, 181
competition, 179
data storage, 177
demographics of, 187
infrastructure of, 186, 192–198
for sale, barter, or lease, 177–178, 180
self-perpetuating, 182–183, 185
transnational hopping and jurisdictional issues, 178, 186, 187
worm production, 171–172
Brand, Stewart, 50
Bridle, James, 112
Brin, Sergey, and Lawrence Page, 120–122
Brunner, John, The Shockwave Rider, 171
“Brutal” files, 73
Burrell, Jenna, 106–107
Business Week, 30
California Business & Professions Code §17529.5, 66
Calvino, Italo, 125
Camouflage tactics, 111–112
Cancelbots, 66, 84
cancelbot wars, 67, 81
CAN-SPAM Act, U.S., xx, 66, 73, 93, 98–99, 118, 142
legitimating influential spammers (view), 99–100
Canter, Lawrence, and Martha Siegel, 1, 48, 53, 61. See also Green Card Lottery message
How to Make a Fortune on the Information Superhighway, 58–59, 60–61, 62
CAPTCHA crackers, 168, 169–170
“culturally restricted” access to, 170–171
Captcha King, 169–170
CAPTCHA systems. See also Turing test
proving you are human, 113, 168, 169
sums for spam solutions to, 169–170
Carnegie Mellon dataset, 131
Charivari, 34, 43, 45, 56, 57, 61, 73, 81, 93. See also NANAE; Vigilantism
anarchistic charivari, 46, 94
defined, 44
political charivari, 46
structure of, 46–47
Chatbots, 176. See also Bot machines
Chess, 63
Chicago School of sociology, 6
“Chunking” approach, 63–64, 66–67
Cialis links, 162
CL Auto Posting Tool, 168
“Clean room(s),” 22
Clickable links, 117–118, 157. See also Keywords; Links
Coate, John, 6
Coevolution of search engines and spam, 67, 113–116, 190
Cohen, William W., 219n9
Commercial ads on the Web. See also Green Card Lottery message
early ads considered spam, 49, 51–52, 61
early network bans on, xx, 53
“legitimate” (see Internet commerce)
Community
defined and features of, xiv, 6–7, 10, 155
Gesellschaft and Gemeinschaft, 31, 59
idea of as a marketing hook, 7
issue of control, 134
media community, 59–60
online community (see Online networks; Virtual community)
supercommunity, 5, 203
Community Memory, Berkeley, 2
Computer contaminants. See Malware
Computer mouse, 30
Computer network architecture, xvii–xviii, xx, 1–2, 4, 10. See also Online networks
design/impact constituencies, xv, xvi–xix
early networks (see also by name, e.g., WELL), 2–10
embedded values, xv–xvi, xviii
social side effects of, 120–121, 123
virtual community dependent on, 12
Consensual space, 13, 15–16
Content farms, 155, 162–164, 166, 197
producing and aggregating sites, 164–166
Cormac, Gordon V., and Thomas R. Lynam
“Spam Corpus Creation for TREC,” 131, 209n4
Corpora building, 126–131. See also Spam corpus
Corporate antispam market, 191
Costs. See Economics of spamming
Counterartifacts, xvii
“COWABUNGA” attack, 48–49
“CPM” (Cost Per a thousand), 164
Craigslist
antispam/spammer arms race, 167–169
automated spam posting engines, 170
Craigslist Bot Pro 1, 168
Credit card scams, 144
Criminalization of spam, 154–155, 177, 180, 190
criminal infrastructure, 186, 192–198
Crispin, Mark, 33–34
Cross-linking, 160
Crowdsourced surveillance, 46
Crowdsourcing, 166. See also Machine distribution of human labor
CTSS (Compatible Time-Sharing System), MIT, 4, 5–6, 27, 28, 63
Culture industry, 166
Cutwail botnet, 184, 186, 193
Cybercafés, xii, 102, 106, 110
Cyberpace. See Internet
“Cyberspace Innkeeping,” 7
d (damping factor), 122–124
DDoS (distributed denial of service) attacks, 56, 94, 175, 178, 189–192, 194. See also DoS
DECNET (Digital Equipment Corporation network), 3
de Groot, Adriaan, 63
Demand Media, 162, 163, 166
Den Beste, Steve, 37
Department of Justice, U.S., 43, 72
Depew, Richard, 41
Design community, xvii
design/impact constituencies, xv, xvi–xix
not a collectivity, xviii
open-source software programmers, xviii
stakeholders in, xviii
Dewey, John, 6, 9
DeWolfe, Chris, 70
Dibbell, Julian, 15, 16, 17, 33
“Digital vernacular” (Lialina), 72
“Distributor model” of spam, 68, 182. See also Information distribution
DMA (Direct Marketing Association), 70
Documents, computer. See also Language; Text
as probabilistic arrangements of words, 136–137, 141
Domain flooding, 158
DoS (denial of service), 174
Dot-com industry, 113–114
boom and collapse, xx
successes, 133
Du Bois, W. E. B.
“double consciousness,” 107
Durkheim, Émile, 6
Early spam and spammers. See under History of spam
Ecommerce. See Internet commerce
Economics of spamming, xxi, 50, 141, 180, 183
Borgesian publishing economics, 127, 183
changes in, 154
Email, 24, 131. See also Spam corpus
attachments releasing worms, 172–173
email cultures, 131–132
email text as objects (see Mechanized semantics)
“junk mail” problem, 21, 24, 27–30, 33–34, 132
message headers, 24–25, 35
precursor to, 27–28
as a private medium, 126–127
Email corpora, 129, 139, 140, 184, 219n9. See also Spam corpus
Email servers
creating false accounts on, 169–170
Encryption, 175
Engelbart, Douglas, 30
Enron
analysis of dataset, 130–131
corporation email (Enron corpus), 71–72, 129–132
Estonia, DDoS attacks, 188–190
Facebook, 65, 80, 111, 165, 168, 178, 194, 197, 202
False positives
defined, 145
as an issue in spam filtering, 137–139, 145
FBI, 192, 193
Features and effects of spam
affecting culture and language, xi–xii
creating a technological drama (see “Technological drama”)
definitions changing and blurring, 39–40, 162–165
distorting the shape of the Internet, 39, 160, 164–165
exploiting tensions between infrastructure and expression, 9–11, 125–126, 199
a means of allocating attention, 112, 203–204
methods and technology (see Infrastructure of spam; by method)
utilizing scarce resources, 37, 39, 56, 64
Feinler, Elizabeth “Jake,” 30, 32, 41
FERC (Federal Energy Regulatory Commission, U.S.), 129
FidoNet, 3
“15 idiots” (Graham), 154–155
Figallo, Cliff, 6
File-sharing programs, 173
Financial factors, xiii
Finjan company, 177
First Class Mail, 66
Flame wars, 8, 51
Flaming, 43, 45, 46, 56, 81, 84
“Floodbots,” 173–174
Forbes, Amy Wiese, 46
Forged return addresses, 69, 71, 73, 135, 138
FOSS (free/libre open source software) movement. See Open-source software movement
“419” messages, 67, 101, 105. See also by name, e.g., “Nigerian Prince” stories
complete with photos, 109–110
victims of characterized, 108–109
Fraser, Matthew, 45
Freedom of speech issue, 57
FTC (Federal Trade Commission), 80, 90, 216n71
Fuller, Matthew, and Andrew Goffey, 66
Future prospects for spam, 180–181, 183–184, 197–198
massive attacks and militarized responses, 187–192
spam immersed in larger criminal process, 188
squibs of text and a single link (Graham), 180–181
Galloway, Alexander, 8
Games, networked, 4
Garst, Rodona, 47, 70–71, 74–80, 90, 91, 96, 192, 197
versus the Man in the Wilderness (See MITW)
General Electric Information Services, 2
GENIE Project, University of California, Berkeley, 4
“Ghost number blocks,” xii, 193
Gibson, William
Neuromancer, 185
Zero History, 111
Glenny, Misha, 108
Glick, Jon, 117
GlobalMedia Design, 173
Global spam machine, xi
Gmail, Google, 140, 194
“Gold standard” for spam, 131, 134
Google search engine, 114–115
dominance of, 113–114
keyword-based advertising systems, 114–115
ranking system 8, 157 (see also PageRank algorithm)
spiders and search/result strategies, 119, 120–124
third generation search/result strategies, 122, 124–125
Governance of the Internet. See Internet governance
Government antispam involvement. See also FBI; FTC; US Postal Service
Internet commerce a priority, 99
Graham, Paul. See also Bayesian filters
on addressing the economics of spamming, 133–135, 141–142, 154
on lowering rates of false positives, 137–139, 145
on machine-machine competition, 134–135
model of naïve Bayesian variants, 129, 135–139, 140, 155, 220nn19–20
“A Plan for Spam,” 101, 133–134, 139–142, 144, 145, 153–154
on the spam of the future, 180–181
Green Card Lottery message, xx, 48, 53–56, 67. See also Canter, Lawrence, and Martha Siegel
response and counter-response, 55–58
Grossman, Wendy, 52, 60
“Growth Hormone” ads, 71
GT&T (Guyana Telephone and Telegraph), 68
Guardia Civil, Spain, 193
Hackers, xvii, 18–19, 36, 71, 85 187
practices of, 133–140
Hardy, Thomas, 44
Harvesters. See Spiders
Harvey, Adam, 111
Hawke, Davis, 69–70, 76, 80, 153
Hayes, Dave, 38
Hess, Elizabeth, 16
Hilderbrand, Lucas, 73
Hippie networks, 2
History of spam, xiv, xx–xxi, 10, 25, 63–64. See also Antispam/spam war; Green Card Lottery message
abuse of early spammers, 64–66
coevolution of search engines and spam, 67, 113–116, 190
explosion of spam, xx–xxi, 66–69
first marketing message, 31
Internet privatization process, xx–xxi, 53
Monty Python skit and naming of spam, 14, 25
protospam messages, 30–31
Quasar robot and sci fi discussions, 27, 29–32, 37, 209n71
reinvention of spam, 143
“scientific” spamming, 125, 184
toad “Minnie,” 16
HITs (human intelligence tasks), 166–167
Hobby networks, 3
Hofstadter, Douglas, 63
Holt, Thomas J., 180
Homebrew Computer Club, xvii–xviii
House of Representatives, U.S., mail servers crashed, 67
HR 1748 Bill, 66, 91, 92
HTML (HyperText Markup Language), 116–117. See also Language; Text
HTML links
cross-linking, 160
link farms for spammers, 122, 158
link stuffing, 156, 157
page link statistics weighting pages, 120, 121, 123, 160, 222 (see also PageRank algorithm)
social graphs of links, 124, 158
Huffington Post content producing and aggregating site, 164
Human-authored spam. See Content farms
Human-machine distinction, 118–119, 120. See also Antispam techniques and methods; CAPTCHA test systems; Computer network architecture; Robot readable media
blurring of the distinction, 162, 164–166
escalating technologies for distinguishing, 170–171
human control vs. human-machine competition, 134–135
Hypertext. See HTML
IBM computer network, 3
Identity theft, 144, 179–180
ifile program (Rennie), 136
Imitation Game (Turing), 149–150, 170
Indexers, 115, 116. See also Search engines
Influence, fake, 165–166
Information distribution, xiv, xvii, 68, 181
and the distribution of spam, 68, 154–155, 173, 182, 202
“distributor model” of spam, 68, 182
viral distribution, 45
Information theft. See Phishing messages
Infoseek search engine, 117
Infrastructure of information technology, 5, 7–8, 94, 126, 175, 186, 199–201. See also Internet; Mechanized semantics
allocation of attention, xiv, 199, 201–204
networking capabilities (see Online networks)
spam exploiting vulnerabilities of, 11, 13–15, 40, 41, 61–62
tension between infrastructure and expression, 9–11
Infrastructure of spam, xii, 71, 80, 125–126, 144, 165. See also Botnets; Spam corpus; Spam methods and technologies
as a criminal infrastructure, xxi, 195–196, 199
parasitic relationships, 159–160, 172
Inktomi search engine, 120
International spam, 101, 102. See also “419” messages
transnational hopping and jurisdictional issues, 178, 186, 187
Internet
both human and machine components (see Human-machine distinction; Language; Mechanistic semantics)
engaged users as spam targets (see Attention; Salience; Targets)
as a mechanical assemblage (see Automation; Computer network architecture; Infrastructure of information technology; Media platforms)
as a (human) community of expression, 1, 7, 112, 202–203 (see also Virtual community)
as a medium of attention, xiv, 162, 199 (see also Attention; Media platforms)
as a “polylogue” (amorphic constituency), xx, 29, 34, 37, 203, 209n70, 229n70
popularization of, 53
Internet commerce, 70. See also Commercial ads on the web; Economics of spam
fluid boundaries with spam, 164–165
goal of protecting while stopping spam, 99
potential destruction by spam, 191
privatization, 42
Internet Direct, 54, 56, 57
Internet governance, positions on, 38–39, 41, 56
anarchists (nongovernance), 11, 15, 33, 39, 40, 46, 55, 94
parliamentarians, 9, 15, 32–33, 141
“process queens,” 16
royalists, 11, 15
technolibertarians, 11, 15, 16, 35, 36, 43, 56, 94, 99
Internet vulnerabilities, 189
democratic nature, 40, 41, 61–62
spam as the exploitation of, 11, 13–15, 40, 41, 53, 61–62, 176, 189
IRC (Internet Relay Chat), 173
C&C (command-and-control) channel, 176
ISPs (Internet Service Providers)
explosion of commercial ISPs, 52, 75
profit-making, 126
spam frequency varying with, 126
Jiffy Gmail Creator, 170
“JJ” message, 41–42, 47–48
Johnson, David R., “Due Process and Cyberjurisdiction,” 45
Johnston, Jessica, 88, 191
Jones, Matt, 110–112
Joselit, David, Feedback, 59
Joy, Bill, 11–12
“Junk faxes,” 65
“Junk mail” problem, 21, 24, 27–30, 33–34, 132
Jurisdictional issues
of antispam players, 193–194
nebulous laws, 70
transnational hopping, 178, 186, 187
Kafka, Franz, 155
“Report to an Academy,” 148
Kazaa file-sharing program, 173
Kelly, Kevin, 170
Kelty, Christopher, xviii, 9
Kendall, Lori, 7
Key cracking, 175, 177
Keyword-based advertising systems, 114, 161–162
Keywords
keyword metrics, 159
popular keywords, 160, 164
Keywords and keyword searching, spam techniques based in
keyword-based message filtering, 33–34, 120, 161–163, 173–174, 177, 116, 121–122
keyword stuffing, 117–118, 122, 124, 157
Kleinrock, Leonard, razor incident, 24, 27
KolotiBablo, 170. See also CAPTCHA crackers
Kraken botnet, 184
Kuvayev, Leo, 66, 80, 163
Labor, machine distribution of, 166–167, 169, 170, 194, 203
Lagos cybercafés, xii, 102, 106
“La machine à gloire” (Villiers de l’Isle-Adam), 155
LambdaMOO, 14, 15–17, 83
Language. See also Algorithms; HTML; Text
Chomskyan grammar, 128
natural language, 116, 118, 127, 130, 147, 219
as object(s) (see Mechanized semantics)
as probabilistic arrangements of words (see also Automated text; Bayesian filters; Litspam), 136–137, 141, 150
programming languages (see also by name, e.g., Lisp language), 13, 139, 140, 145
spam vernacular (see also Preprogrammed text), 180–181
typo-ridden languages, 177, 178
user-characteristic vocabularies, 146
Latour, Bruno, 64
Laughlin, Robert, 133
Lave, Jean, 6
Le Corbusier (C.-É. Jeanneret), xvi
Legislation. See Antispam legislation
Lialina, Olia, 72
Licklider, J. C. R., 34
and Robert Taylor, 1
LIDAR (Light Detection and Ranging), 111–112
Ling-Spam corpus, 128
Linguistic recursion, 128
Linkbaiting, 165
Linkless gibberish messages, 146
Links. See HTML links
Link trading, 157
Lisp language, 133, 139
Litspam
fooling Bayesian spam filters, 148, 149–150, 162–163
literary texts used, 143–148, 149
marketing links included, 151
Machine distribution of human labor, 166–167, 169, 170, 194, 203
Machine-machine competition, 134–135. See also Algorithms
Macro/micro moments, 68
Malware, xiii, 144, 172, 181, 183, 185, 186, 187, 189
self-propagation of, 179–180, 182
malware coders, 197
Mandel, Tom, 51
Mann, Merlin, 165
Mariposa botnet, 193
Market and profitability for botnets, 175, 177–180
Marketing messages, 31, 36
Marwick, Alice, 166
Mass emails, 70
“Massification” (Fuller and Goffey), 66
Master, The (Nigerian video), 109–110
McCarthy, John, 33
McColo Hosting Solutions, 193–194, 195–196
McLuhan, Marshall, 6
McNeil, Joanne, 112
McWilliams, Brian, Wired, 65
Mechanical Turk, 166–167, 203
Mechanized semantics, 115, 147. See also Human-machine distinction
automated text, 41, 159, 161–162, 168, 174
Bayesian probability applied to (see Bayesian filter)
the document as an object, 13–14, 127, 135–136, 136–137
lexical analysis, 127–128
posthuman semantics, 162
robotic vs. human readability, blurred borders, 112–113, 169–170
text content farms, 155, 162–164, 166, 197
words as tokens, 136
Media community, 59–60
Media files, sharing, 173
Media platforms, 59, 198, 204
design constituencies, xv, xvii
“Megamachines,” xvii
Messaging through proxies, 100, 135, 168, 191, 193
“Metalogue,” 50
“Meta tags,” 114, 116–117. See also HTML
Metered email, 66
METHICS, 35
Metrics systems, 164
keyword metrics, 159
Microchip fabrication, 22
Microsoft products, 89, 113, 140, 173, 224. See also PCs
Military uses of computer networks, 20, 22–23. See also ARPANET
MILNET, 49
Minitel, 3, 201
Mitnick, Kevin, 190
MITW (“Man In The Wilderness”), 71, 72–73
Monaco, Principality of, xi
Monty Python skit, 12–13, 14, 25, 65, 198, 207n28, 216n78
MOO (MUD Object-Oriented), 13–15, 16
Mosaic web browser, 53
Motion sensors, 111–112
MUD (Multi-User Dungeon), 13
Multics system, 6
Mumford, Lewis, xvii
Mydoom worm, 174–175, 181, 186, 223n84. See also Worms
MySpace bots, 170
NANAE (news.admin.net-abuse.email), 65–66, 77, 79, 81, 173, 192, 193, 203
founding of, 93–94
vanguard antispam coalition, 94, 99
NATO, 190, 192, 194
NATO Cooperative Cyber Defense Center of Excellence, 190
Natural language, 116, 118, 127, 130, 147, 219
Naughton, John, 4
Naver search engine (Korea), 114
Nelson, Ted, xvii–xviii, 34–35
“Net abuse,” 39
Netblocks (range of Internet addresses), 178
Networked games, 4
“New Aesthetic” (Bridle), 112
Newsgroups, 38
NIC (Network Information Center), 30
“Nigerian Prince” stories, xxi, 67, 102, 108–109, 180
African socioeconomic conditions and, 105–106, 109
“double consciousness” and, 107
Nissenbaum, Helen, 9
NNTP (Network News Transfer Protocol), 49
Noha, Robert (“JJ”), 41–42, 47–48, 60
“Nollywood” video industry, 109–110
Nonsalient intrusions into networks, 48, 54
Nonspam context (“ham”), 126
NSF (National Science Foundation), 49
ending ban on commercial activity, 53
NSFNET, 49, 51
Obfuscation process, 127, 129, 219
Object-oriented language, 13–14
Ochoa, D. Santiago de story, 104
Online community. See Virtual community
Online networks. See also Computer network architecture; Open-source movement; Virtual community
broadband connections, 172
commons-based peer production (early), 58
international packet-switching, 2
“netiquette” on, 40, 164
“network forum” concept, 50
the rule of salience, 48, 60–61, 201 (see also Relevance)
telephonic dialup vs. cable access, 186–187
Ontological statements, 26–27
Open network discourse, 22–23, 27, 36, 49
Open relays, 93, 135
Open-source software movement, xviii, 9, 18, 23, 29, 140, 223–224n84
PageRank algorithm (Google), 8, 112, 120–121, 157–158, 160, 167
spam-fighting equation, 122–124
spammers’ response to (see Splogs/splogging)
PageRank greenhouses, 160–161
page views, number of, 167–168
Pantel and Lin, 137
Parasitic relationships, 159–160, 172. See also Worms
Parliamentarians, 9, 15, 32–33, 141
Passwords
breaking, 175
fake requests (phishing messages), xxi, 76, 178
Pattern-matching software, 183
PayPal account spammers, 155
PCs (personal computers), 3, 186, 192. See also Personal computer
PDP (Programmed Data Processor), 30–31
Peer-to-peer system, 173, 182
“Performative ontology,” 26–27
Personal computer, xvii–xviii, 3
Personality spamming, 158, 165–166. See also Self-endorsement
Peters, Tim, 140
Pfaffenberger, Bryan, xiv–xv, xvi, 38
Phantom content. 158, 160, 163. See also Automated text
fake blogs, 155–157, 159–161
fake influence, 165–166
fake relevance, 164–165
fake requests, xxi, 76, 178
false email accounts, 169–170
false return addresses, 71
false social structures, 158–159
Phishing messages, xi, xxi, 76, 108, 178
Phone sex emails, 67–68
Pickering, Andrew, 26, 27
“Pinch” software, 179–180
Pitcairn Island, xi, xiii, 187
Political charivari, 46. See also Charivari
“Polylogue.” See under Virtual community
Pornography, 71, 149, 226n1
Portal.com (Portal Information Network), sysadmins’ delegation of responsibility, 42–44
Postal fraud, 107–108
Postel, Jon, 20, 25–26, 30
Postimees, 188
Premier Services, 70–71, 72–73
Preprogrammed text, 174. See also Automated text
Pricing proposals, 99
Privacy issue, 126
Privatization of the Internet, xx–xxi, 53
Procmail scripts, 66
Programming languages (see also by name, e.g., Lisp language), 13, 139, 140, 145
Proxies
advertising through, 168
messaging through, 100, 135, 168, 191, 193
Proxy bots, 224n94
Proxy servers, 100, 135, 168, 191, 193
Pseudorandom number generators, 185
Public, the. See Users
“Pump-and-dump” schemes, xxi, 71, 79–80, 90
“Push media” model, 114
Pyra Labs, Blogger, 158–159
Python programming language, 139–141
Python-Dev mailing list, 139–140
“QL” sites (WWII), 161
“Quantified audience,” 161–162, 163
Query handlers, 115, 116. See also Search engines
Ralsky, Alan, 66
Random surfer (homme moyen), 122, 123
Raymond, Eric, 140
Reactive publics, 9–10
Real-time messaging, 173, 176
“Recursive public,” 9
Regulation of technology, xxi
territorial boundaries and, xxi
Relevance
fake relevance, 164–165
the rule of salience, 48, 60–61, 201
search engines seeking, 121–122
Rennie, Jason, 136
Reputation factor
in page ranking, 123–124
“reputation economy,” 157
social graphs, 158
ResponseBase, 70
Revenue algorithms and metrics, 162, 164
RFCs (Requests for Comments), 20–21, 25–26, 36
Rheingold, Howard, 7, 60
Richter, Scott, 66, 100–101
Ritchie, Dennis, 5
Robotic machines. See Bot machines
Robotic readability, 119–120, 148, 149–150. See also Litspam; Mechanized semantics; Search engines
blurred edges with human readability, 112–113, 169–170
robot readable media, 110–112
ROKSO antispam organization, 66
“Root paradigms,” xviii–xx, 86
Rosenfeld, Morris, 143
Roth, Daniel, 163
RoverBot, 173–174
RSS (Really Simple Syndication), 159
SAGE (Semi-Automatic Ground Environment), 2, 3, 4, 5, 22–23, 30
Sakaguchi, Nelson, 108
Salience. See also Attention; Keywords
nonsalient intrusions, 48, 54
rule of salience (early), 48, 60–61, 201
salient communities as vulnerable targets, 112
“Scientific” antispamming, 125–133, 184
Scott, James C., xvii
Search-based spam techniques
“cloaking,” 119–120
goal of improving page rank, 167
hidden and bi-face text, 117–119, 120
keyword-based message filtering, 33–34, 120, 161–163, 173–174, 177
keyword stuffing, 117–118, 122, 124, 157
link farms, 122
Search engine optimization (SEO), 115, 164–165
Search engines
coevolution of search engines and spam, 67, 113–116
Google dominance (see Google search engine)
linkage statistics weighting pages, 120, 121, 222
“relevance” question in, 121–122
Search strategies
first-generation strategies, 116–120
meta tags’ use and discontinuance, 114, 116–117
second-generation strategies, 120
third-generation strategies, 120, 122, 157, 158
Self-endorsement, 158, 165–166
Self-propagation of malware, 179–180, 182
Storm’s capacity, 182–183, 185
Shaming, 43, 44, 46, 73, 81, 84
Shaviro, Steven, 14
Shoch, John, and Jon Hupp
“The ‘Worm’ Programs . . ., ” 171–172
Signature patterns, 158
Silicon Valley, 22
“Skinny Dip” fat-loss cream ad, 67
Skirvin, Tim, 82, 94
Skype, 168
Smith, Daniel Jordan, 105, 106
Social corruption
419 spam as indicative of, 105, 106
Social networks, 167, 173
“floodbots,” 173–174
Social recommendation systems, 167
Social structure, 157–158
creating false, 158–159
“social spam,” 61, 155, 163
Sockpuppetry, 8
Software. See also by program
free (“cracked”) copies of, 173
software tools, 66
worm writing, 176
Soloway, Robert A., 66, 100
Sorkin, David E., 100
Spam
as fake information (see Phantom content)
future of (see Future prospects for spam)
history of (see History of spam)
how it works (see Features and effects of spam)
the Internet and (see Infrastructure of spam; Internet vulnerabilities)
major variants and developments of (see Botnets; Mechanized semantics; Viruses; Worms)
the profit-making motive and (see Criminalization of spam; Economics of spam; Internet commerce)
technology of (see Infrastructure of spam; by method)
SpamAssassin, 128, 138
Spambots, xiii, 41, 133, 144, 155–157, 159, 174–175, 186–187, 192–193, 196, 199. See also Automated spam; Botnets; Splogs/splogging; by name, e.g., Terra, Terra’s blog
SpamCop, 137
Spam corpus, 126–128, 131, 209n4, 219n9. See also Email corpora
Enron corpus, 71–72, 129–132
Spam filters, xxi, 97, 133, 138, 141–142
Bayesian algorithms (see Bayesian filters)
bogofilter, 139–140
economic rationale for, 133–134
filter-beating algorithms, 162–163
fixed vs. flexible filtering elements, 137–139
issue of false positives, 137, 139, 145–146
methodological problems, 126–129, 132
“polymorphism” as spammers’ response to, 182
technolibertarians advocating, 99
Spam-free zones, 194
Spamlaws.com, 100
Spammers. See also by name, e.g., Canter, Lawrence, and Martha Siegel
abused in early period, 64–65
Achilles’ heel of (Graham), 141
avant-garde of, 197–198
“bottom-feeders,” distinct from opt-in, 142
economic issues of, 133–135, 141–142, 154
Spam robots. See Spambots
Spam stories and characters, 102–104. See also “419” messages
Spanish Prisoner stories, 103–104
Spiders, 115–116, 119–120. See also Search engines
second-generation spiders, 121
Splogs/splogging
automated, 155–157, 159–161
combined with human labor, 166–167, 170
content farms, 155, 162–164, 166, 197
excerpt splogs, 160
Srizbi botnet, 184, 186, 193
Stack Overflow, 114
Stallman, Richard, 29, 32–33
Stephenson, Neal
Anathem, 18
Cryptonomicon, 147
Storm botnet and worm, 180–182, 184–185, 186
address harvesting feature, 183
flaw in the system, 185
headline hooks, 181
infrastructure, 181–182
self-propagation capacity, 182–183, 185
Stross, Charles, 150
Supercommunity, 5, 203
Symbolic orders, 111–112
Targets
Internet users as, xiv, 31, 100, 180, 182, 187
salient communities vulnerable, 112
“target lists,” 176, 178–179
types most vulnerable as, 155, 162
Taylor, Robert, 1, 4, 27
Technolibertarians, 11, 15, 16, 35, 36, 43, 56, 94, 99
advocating filters (see Spam filters)
“Technological drama,” xiv–xv, xvi–xvii, xx, 115, 163, 201
Technology, values embedded in
aviation example, xv–xvi
Sri Lankan irrigation project, xvi–xvii, xviii
Telephone networks, 3
Telephone verification, 168
spammers generating phone numbers, 168–169
Tension between infrastructure and expression, 9–11
Terra, Terra’s blog, 155–157, 159, 160
Text. See also Bayesian filters; Blogs; Language; Splogs/splogging
duplication of, 168
preprogrammed text, 174
probabilistic arrangements of (see also Automated text; Bayesian filters; Litspam; Spam corpus), 136–137, 141, 150
textual polymorphism, 168
user-characteristic vocabularies, 146
Throwaway domains, 69
Thuerk, Gary, and Carl Gartley, 30
Timesharing systems, 4, 5, 28
Tokelau, xi
Tokens/tokenization, 127. See also Mechanized semantics
words as tokens, 136
Tönnies, Ferdinand, 6
Train, Arthur, 103–104
Trolling and griefing (strategic provocation), 8
Turing, Alan, 170
Imitation Game (Turing), 149–150, 170
Turing test, 148, 149–150, 170. See also CAPTCHA test
Turkle, Sherry, 12
Turner, Victor, xviii–xix
Twitter, 165, 197, 203
“Tyler” (keyword), 160
Typo-ridden languages, 177, 178, 178–179
Unix operating system, 19, 35–36
UN letterheads, 104
“Unsubscribe me” options, 100
Usenet, xx, 1, 8, 35, 52
antispam battle on, 67
AOL users can access, 52
debates over proper use of, 37–40
infrastructural imbalances, 36–37
a new form of discourse on, 37–38
Usenet democrats, 40, 41
Users, 5, 7, 9–10
defining spam differently, 132
distinctive patterns of use, 129, 145–146
social and professional bonds, 130
user educators, 99
user-produced content tools, xix
users, 5, 7, 9–10
U.S. military botnet (advocated), 190
U.S. Postal Service, 107
U.S. Post Office, Remote Encoding Center, 113
UUCP (UNIX-to-UNIX Copy Protocol), 49
Van Vleck, Tom, 6, 27–28, 209n67
Viagra ads, 67, 102
“Victim cloud,” 176
Vigilantism, 43, 46, 56–57, 88, 99, 185. See also Charivari
vigilante-spammer struggles, 66–67, 71, 72–73
viral vigilantism, 44–45
Villiers de l’Isle-Adam, Auguste, 155
Viral distribution, 45
“Viral infection,” 89
Viral media, 197
Virtual community, 1, 5, 7–9, 58, 112, 121, 134. See also Community; Online networked; Users
“contextual integrity” in, 9
culture of the web, 65–66
dependent on mechanical infrastructure (see Computer network architecture)
as design constituencies, xv, xvi–xix
feedback loop with spammers, xii
fragility of, 7–8
as “not a community at all” (Canter and Siegel), 53, 59
open-ended networking, 27, 36, 49
as a “polylogue,” xx, 29, 57, 203, 209n70, 229n70
random surfers (homme moyen), 122, 123
Viruses, 89, 138, 154
antivirus software, 89, 181, 186, 188, 191
vi text editor, 11–12
Voice-over-Internet (VoIP), 169
Vollybllgrl’s blog, 156
Walker, Steve, 24
Wallace, Sanford, “Spam King,” 64–65, 66, 80, 90, 142
WC3 (World Wide Web Consortium), 53
Web. See Internet
Weblogs, 158–159. See also Splogs/splogging
Websense, 169
WELL (“Whole Earth ’Lectronic Link”), 1, 3, 10, 16, 49, 50–51, 60, 82
Wells, H. G., xvi, 126
Wenger, Etienne, 6
Werry, Chris, 58, 59
Who command, 6, 209n67
Wiener, Norbert
Cybernetics, 133–134, 135
The Human Use of Human Beings, 134
Williamson, Charles W., III
“Carpet bombing in cyberspace,” 187, 190
Williams, Raymond, 7
Wizard, The (company), 67, 78
Wizards, 17–20, 27, 35, 57, 93, 94
as “barons,” 39, 40
Gandalf metaphors, 18, 97, 194
at MIT, 27, 30
power through capability, 18, 21, 22
Unix wizards, 35
wizardly/anarchist mode, 56
Wopla botnet, 184, 186
World Wide Web. See Internet
Worms, 183–184, 187, 223–224n84. See also Botnets; Malware; Mydoom worm; Storm worm
including antimalware kits, 183
worm production, 171–172
worm writers, 176
Xanadu, 34–35
Yahoo!, 114
Yandex search engine (Russian), 114
Y Combinator, 133
“Zombie” machines. See Bot machine