Speaker 1: Have you ever wondered what truly lies beneath the surface
Speaker 1: of the Internet, you know, far beyond what traditional search
Speaker 1: engines like Google can ever show you.
Speaker 2: It's a massive space. Really, we're talking about these vast
Speaker 2: unseen digital realms.
Speaker 1: Right, teeming with fascinating, sometimes alarming, and often incredibly useful information. Absolutely,
Speaker 1: welcome to the Deep Dive, the show where we cut
Speaker 1: through the noise, unpack complex topics and extract those vital
Speaker 1: nuggets of knowledge so you can become well informed, quickly
Speaker 1: and thoroughly.
Speaker 2: And our mission today is really to give you a
Speaker 2: powerful understanding of the digital world's hidden layers, both.
Speaker 1: The Deep Web and you know, the more notorious Dark
Speaker 1: Web exactly.
Speaker 2: And we're diving into a truly comprehensive resource today, the
Speaker 2: Dark Web Breakthroughs in Research and Practice.
Speaker 1: Ah okay, Yeah, it's a.
Speaker 2: Compilation published by IGI Global back in twenty eighteen, and
Speaker 2: it's packed with cutting edge theories and developments. It's really
Speaker 2: designed to empower anyone wanting a deeper understanding of this
Speaker 2: whole evolving space.
Speaker 1: Sounds perfect. So this incredible resource is organized into four
Speaker 1: major sections.
Speaker 2: That's right, cybercrime and security, then data mining, an analysis,
Speaker 2: online identity, and finally web crawling.
Speaker 1: Okay, so we're going to navigate these areas pulling out
Speaker 1: the most surprising facts and relevant details for you, helping
Speaker 1: you see the unseen.
Speaker 2: Let's do it.
Speaker 1: Okay, let's unpack this first section then, on cybercrime and security.
Speaker 1: It highlights some truly well eye opening developments in online
Speaker 1: criminal activity.
Speaker 2: Yeah, and what's fascinating here, I think, is how the
Speaker 2: research goes beyond just describing the crimes. It really seeks
Speaker 2: to understand the underlying psychological and social factors driving them.
Speaker 2: Like what for instance, well, take the unsettling context of
Speaker 2: revenge porn. The research introduces something called the dark triad
Speaker 2: personality traits.
Speaker 1: The dark triad that sounds well, pretty ominous. What exactly
Speaker 1: are those traits?
Speaker 2: It refers to machiavelianism, psychopathy, and narcissism. And these aren't
Speaker 2: just buzzwords, you know. They represent characteristics like callousness, egocentrism,
Speaker 2: low empathy, and a well a readiness to exploit others.
Speaker 1: Okay, so break those down a bit.
Speaker 2: Psychopathy is psychopathy specifically indicates a severe lack of empathy,
Speaker 2: impulsivity really driven by immediate.
Speaker 1: Gratification and machiavilianism.
Speaker 2: That's more about strategic ruthless manipulation, planning things.
Speaker 1: Out, got it and narcissism.
Speaker 2: That's all about entitlement, grandiosity and ego reinforcement, needing that validation.
Speaker 1: So what's the link to revenge porn?
Speaker 2: Well, the significant finding here is that endorsing these dark
Speaker 2: triad traits strongly predicts a greater propensity for engaging in
Speaker 2: revenge porn and disturbingly, also a greater enjoyment of tormenting
Speaker 2: others online.
Speaker 1: Wow, it's chilling. It is a stark look at the
Speaker 1: motivations behind some of the Internet's darkest corners.
Speaker 2: It really is.
Speaker 1: That's a powerful insight into individual psychology. But moving on
Speaker 1: to a broader, maybe more societal scale of online malice,
Speaker 1: the source also delves into temporary terror on the net.
Speaker 1: This isn't just about loan actors, is it.
Speaker 2: No, definitely not. It's a significant shift we've seen extremist
Speaker 2: groups have evolved from relying on singular leaders to leveraging
Speaker 2: these vast decentralized networks lots of loose weak ties basically
Speaker 2: to spread their ideology and tactics.
Speaker 1: And is the Islamic state is a key example.
Speaker 2: A stark example. Yes, they actively manipulate a concept called
Speaker 2: the constitutive. Other essentially, they prey on feelings of isolation.
Speaker 1: Oh so who do they target.
Speaker 2: They specifically target younger Middle Eastern women in the Western world,
Speaker 2: luring them with this promise of an Islamic state where
Speaker 2: they'll supposedly feel understood and you know, part of a family.
Speaker 1: So they're exploiting personal vulnerabilities for ideological recruitment, creating a
Speaker 1: sense of belonging in a very dangerous way.
Speaker 2: Precisely, this shared emotional state is crucial for contemporary social
Speaker 2: movements and global ghod. The research also details lone wolf terrorism,
Speaker 2: noting its core with the proliferation of powerful weapons.
Speaker 1: Right, and these individuals often blend ideology with personal issues.
Speaker 2: Exactly deeply personal grievances. Think about incidents like the Orlando
Speaker 2: nightclub shooting or the Fort Hood attack. As Corner noted
Speaker 2: back in twenty sixteen, these are often disturbed individuals who
Speaker 2: sort of layer a political facade over their personal problems.
Speaker 1: And this decentralized network structure, it sounds like it makes
Speaker 1: these organizations incredibly difficult to combat.
Speaker 2: It absolutely changes the game with no specific heart or
Speaker 2: head that can be targeted. As the source puts it,
Speaker 2: traditional counter terrorism strategies become well less effective.
Speaker 1: How did iss manage their media? Then?
Speaker 2: They built an incredibly sophisticated media strategy. They use their
Speaker 2: monthly Debak magazine, for instance, published via the dark web
Speaker 2: for anonymity, and they varied their social media content widely,
Speaker 2: everything from you vicious beheading videos to seemingly innocuous kitten.
Speaker 1: Memes kitten memes really.
Speaker 2: Yeah, designed to appeal to different demographics for recruitment, and
Speaker 2: when faced with crackdowns like Twitter suspensions, they adapted incredible quickly.
Speaker 2: How they shifted from user centric dissemination where you could
Speaker 2: trace it back to one account, to a hashtag driven model.
Speaker 2: This made their messages far harder to trace.
Speaker 1: The dark web then becomes an essential, if hidden publishing
Speaker 1: platform for them.
Speaker 2: That's absolutely right. It's decentralized and anonymous networks are just
Speaker 2: crucial for propaganda, especially after activist Wars pushed a lot
Speaker 2: of extremist Islamic discourse onto these hidden platforms.
Speaker 1: And it shares technology with other known dark websites.
Speaker 2: Yeah, it shares some of the same technological underpinnings as
Speaker 2: places like wikileaps, Bitcoin, and the infamous Silk Road.
Speaker 1: That's a stark picture of the threats lurking in the
Speaker 1: digital shadows. Let's switch gears a bit. Maybe something many
Speaker 1: of us can relate to more directly. Dysfunctional digital behaviors
Speaker 1: in online learning.
Speaker 2: Ah. Yes, the elephant in the online classroom.
Speaker 1: Horse calls it that, right, and it's apparently far more
Speaker 1: prevalent than we might realize.
Speaker 2: It's a very apt metaphor because it's often ignored, isn't it.
Speaker 2: This elephant covers everything from cyberbullying and plagiarism to outright
Speaker 2: hacking and just the constant search for shortcuts.
Speaker 1: Can you give an example like the plagiarism case.
Speaker 2: Sure, the research describes a student a who plagiarized a
Speaker 2: term paper. The reasons well, conflicting priorities. They had a
Speaker 2: demanding agency report due at the same time, but also
Speaker 2: the sheer ease of.
Speaker 1: Copy pasting, and the distance factor.
Speaker 2: Exactly the perceived distance from the instructor. The student actually
Speaker 2: rationalized it thinking it was easy since my instructor was
Speaker 2: thousands of miles away.
Speaker 1: Wow, and hacking isn't just for big corporations. It happens
Speaker 1: in e learning too. That's pretty unsettling.
Speaker 2: It is the source details specific methods, students logging in
Speaker 2: as an instructor to grab test answers, using spyware to
Speaker 2: see others answers during tests, or even employing sniffers to
Speaker 2: decipher network packets for passwords.
Speaker 1: So online learning can become less about education and more
Speaker 1: about winning for some students, like a game.
Speaker 2: That's the core of this gamer's agenda concept they talk about.
Speaker 2: Students start treating online courses like games they need to
Speaker 2: win by quote outsmarting.
Speaker 1: The system, and they rationalize it, how.
Speaker 2: Often through an avatar identity, believing the person cutting corners
Speaker 2: isn't really them. The research lists key reasons pressure to
Speaker 2: maintain high GPAs the feeling they probably won't get.
Speaker 1: Caught, the ease of copy pasting you mentioned.
Speaker 2: Right, the perception that everyone does it, and unfortunately sometimes
Speaker 2: a noticeable level of faculty apathy or at least perceived apathy.
Speaker 1: And distance seems to play a significant role here.
Speaker 2: Oh, absolutely. The research shows a clear inverse relationship. The
Speaker 2: greater the distance between student and teacher, the higher the
Speaker 2: tendency for cheating. Less interaction less oversight.
Speaker 1: Which leads to this problematique map.
Speaker 2: Yes, exactly. It's this tangled web of interrelated problems cheating, cyberbullying,
Speaker 2: cutting corners, all linked by these overarching factors like physical
Speaker 2: and psychological distance, the technology itself, cyber psychological elements, and
Speaker 2: broader sociocultural influences.
Speaker 1: And the end result can be quite negative.
Speaker 2: Often it culminates in an adversarial teacher learner relationship, which
Speaker 2: is the opposite of what education should be.
Speaker 1: This really sounds like it demands a fresh approach to
Speaker 1: online education policy.
Speaker 2: It certainly calls for a shift. The implications include moving
Speaker 2: away from just testing factual recall lower level cognitive stuff
Speaker 2: towards fostering metacognitive learning objectives waiting, essentially teaching students how
Speaker 2: to learn and think critically rather than just memorizing facts.
Speaker 2: It also means ensuring assessment doesn't actually get in the
Speaker 2: way of genuine learning and promoting educational policies consistent with openness,
Speaker 2: things like learner centeredness, connectivism really encouraging active construction of knowledge.
Speaker 1: Okay, our next topic in this section asks a pretty
Speaker 1: provocative question, how to become a cyber criminal? It really
Speaker 1: gets into the difference between what hacking originally meant and
Speaker 1: the darker path it often takes today.
Speaker 2: That's such a crucial distinction. Initially, you know, hacking was
Speaker 2: often seen as innovative, even constructive. Think Dennis Ritchie and
Speaker 2: Ken Thompson creating you and IX, or maybe Sean Fanning
Speaker 2: creating Napster. That kind of thing, right, creative problem solving exactly.
Speaker 2: But then it evolved or devolved perhaps into cracking, which
Speaker 2: involves serious criminal offenses.
Speaker 1: And the motivation there is often financial.
Speaker 2: Often yes, the source sites examples of individuals, frequently teenagers,
Speaker 2: driven by the potential for significant financial gain, like the
Speaker 2: s toost botnet, which alone generated and estimated fourteen.
Speaker 1: Million dollars fourteen million. Wow, So it's about the money.
Speaker 1: Often driven by a kind of economic cost benefit analysis,
Speaker 1: the perception that the risk is low but the rewards
Speaker 1: are high.
Speaker 2: Pretty much according to G. Beecker's Economic Approach to Crime
Speaker 2: from way back in nineteen sixty eight. Individuals, especially teens,
Speaker 2: might make that kind of calculation. They perceive hacking or
Speaker 2: cracking as relatively riskless and highly compatible with their lifestyles,
Speaker 2: particularly if they have low earnings or lack other opportunities.
Speaker 1: And the media plays a role indirectly.
Speaker 2: Yes, the potential for huge profits like bought Masters earning
Speaker 2: millions annually gets highlighted, acting as an observability factor, basically
Speaker 2: showing us how lucrative it can be.
Speaker 1: And modern technology like cloud computing makes it even easier
Speaker 1: for cyber criminals to operate.
Speaker 2: It drastically lowers the bar for entry. Yeah. Infrastructure as
Speaker 2: a service or ISS provides massive computing power on demand
Speaker 2: for things like brute force password attacks.
Speaker 1: See you don't need your own supercomputer exactly.
Speaker 2: It enables cheap and large scale denial of service or
Speaker 2: DOS attacks, which can cripple websites, and it simplifies spamming
Speaker 2: or malware distribution via mail. Software as a service. Basically,
Speaker 2: it provides accessible, powerful tools for illicit activities.
Speaker 1: Which also means we're seeing more widespread ransomware and sextortion,
Speaker 1: things that affect ordinary people and businesses Precisely.
Speaker 2: Businesses often fall victim to ransomware, where their data is
Speaker 2: encrypted and held hostage. They frequently pay up, sometimes just
Speaker 2: to avoid the public exposure or the catastrophic data loss.
Speaker 2: Hand sextortion similarly, online sexual extortion or sextortion is sadly
Speaker 2: on the rise, with victims often paying to avoid the
Speaker 2: publicity and shame. The shift from sort of non criminal
Speaker 2: hacking exploration to outright criminal activity is unfortunately facilitated by
Speaker 2: hacking's compatibility with youth culture, its perceived advantages over traditional crime,
Speaker 2: its ease of use, and even the existence of supportive
Speaker 2: online communities.
Speaker 1: Okay, let's shift gears again. Here's where it gets really interesting.
Speaker 1: I think this next section explores how we can actually
Speaker 1: navigate and extract valuable information from the web's hidden depths.
Speaker 1: After all that talk of threads, this is about harnessing
Speaker 1: its power, right And.
Speaker 2: If we connect this to the bigger picture, understanding these
Speaker 2: extracted techniques is crucial for harnessing the vast amount of
Speaker 2: high quality data that lies beyond traditional search engine reach.
Speaker 2: This is the deep web we're talking about now.
Speaker 1: And just to clarify again, the deep web isn't the
Speaker 1: same as the dark web, right correct.
Speaker 2: The dark web is a smaller, hidden part of the
Speaker 2: deep web that requires specific software like tor to access.
Speaker 2: The deep web itself is much larger. It's simply all
Speaker 2: the information stored in searchable databases that dynamically generate results
Speaker 2: when you query them. Think library catalogs, internal corporate sites,
Speaker 2: that sort of thing.
Speaker 1: Okay, So how do we efficiently query these massive deep
Speaker 1: web databases when traditional search engines can't really handle it well?
Speaker 2: The source proposes a rather clever solution, an optimal query
Speaker 2: generation mechanism based on random ranking.
Speaker 1: Okay, random ranking? How does that work?
Speaker 2: Think of it like a smart librarian trying to find
Speaker 2: specific information in a massive archive without pulling every single book.
Speaker 2: It uses a response analyzer and a query ranker. First,
Speaker 2: a form analyzer figures out the structure of the search
Speaker 2: forms on these hitting databases. Then the query ranker prioritizes
Speaker 2: which queries to send based on factors like past query behavior,
Speaker 2: what worked before, and even the size of recent search
Speaker 2: results pages. Then you give an example, sure in an
Speaker 2: air travel database, say this system can automatically reduce illogical
Speaker 2: query combinations like trying to find flights from Deli to
Speaker 2: Deli from maybe eight possibilities down to four by using
Speaker 2: external knowledge like that, it minimizes the number of queries
Speaker 2: needed and avoids duplicates.
Speaker 1: That saves a lot of time and resources.
Speaker 2: Presumably exactly the goal is to exhaustively retrieve the content
Speaker 2: with the minimum number of queries.
Speaker 1: That's incredibly smart, and the Web itself, of course, has
Speaker 1: been constantly evolving, changing how this hidden data is structured.
Speaker 1: The source looked at that too.
Speaker 2: It absolutely has. An analysis comparing the global web in
Speaker 2: twenty nine to twenty fourteen show some really significant changes
Speaker 2: in web page development.
Speaker 1: Such as well.
Speaker 2: While core HTML tags like head, HTML, body, and title
Speaker 2: remained consistently present almost near one hundred percent, tags used
Speaker 2: for dynamic content things like meta, div, link and script,
Speaker 2: their usage increased.
Speaker 1: Dramatically, and older tags decreased.
Speaker 2: Yeah, table related tags like Freya table teap body TD
Speaker 2: their use went down. It clearly signals a move away
Speaker 2: from simple static pages towards more interactive, data driven web experiences.
Speaker 1: What about content formats? Are we seeing shifts there too?
Speaker 1: Images documents, definitely.
Speaker 2: For images, JPG remained dominant, but P and G usage
Speaker 2: grew massively. It jumped from just over three percent of
Speaker 2: all images in two thousand and nine to nearly a
Speaker 2: quarter by twenty fourteen. Wow, and the percentage of pages
Speaker 2: using at least one PNG image nearly quadrupled. This reflects
Speaker 2: a growing demand for richer visual content, more complex web designs.
Speaker 2: JIFF usage meanwhile, pretty much.
Speaker 1: Dropped off, and music documents.
Speaker 2: For music MP three just stayed overwhelmingly dominant over ninety
Speaker 2: one percent by twenty fourteen. Not much evolution there, probably
Speaker 2: due to its excellent quality to size ratio. For documents,
Speaker 2: PDF was the most common and grew steadily, with XML
Speaker 2: also increasing both our faise i've heard for portability and
Speaker 2: professional use.
Speaker 1: Interesting about compression too, Zip is surpassing gztip.
Speaker 2: Yeah, potentially linked to the dominance of Windows operating systems,
Speaker 2: the researchers suggest.
Speaker 1: And how quickly your pages being updated? Does that impact
Speaker 1: how we search or crawl this hidden web?
Speaker 2: This is a huge trend. First, style wise, bold text
Speaker 2: usage decreased, while title styles like H two and H
Speaker 2: three increased, maybe a shift in presentation norms. Average URL
Speaker 2: length stayed consistent, but the percentage of longer URLs grew
Speaker 2: may be indicating more complex content paths.
Speaker 1: But the age of pages.
Speaker 2: Right, here's the crucial part. There's a rapid trend of
Speaker 2: updating content. By twenty fourteen, something like seventy percent of
Speaker 2: pages were.
Speaker 1: Less than three months old seventy percent.
Speaker 2: And nearly two thirds or less than one month old.
Speaker 2: This implies a critical need for much faster recrawling and
Speaker 2: index updates by search engines. Otherwise the results you get
Speaker 2: quickly become outdated.
Speaker 1: So crawlers need to constantly adapt to these dynamic changes.
Speaker 1: How has the underlying technology of web pages affected this?
Speaker 1: Like JavaScript?
Speaker 2: Precisely, the average which number of links per page shot
Speaker 2: up significantly from about fifty eight to over one hundred
Speaker 2: and seven between twenty nine and twenty fourteen. Dynamic links
Speaker 2: also saw substantial growth, and JavaScript critically. Yes, JavaScript became
Speaker 2: the most dominant client side technology, hitting nearly seventy six
Speaker 2: percent usage by twenty fourteen. Older techle like Flash, bbscript,
Speaker 2: tclscript they nearly disappeared. Why The shift driven largely by
Speaker 2: the rise of ajax, asynchronous JavaScript and XML, and also
Speaker 2: flashes well known security and compatibility issues. What this means
Speaker 2: is that modern crawling systems must primarily focus on processing
Speaker 2: JavaScript effectively to index the web properly today, and on
Speaker 2: the server side, Php interestingly remain dominant there.
Speaker 1: Okay, this all leads nicely into the deep web information
Speaker 1: retrieval process. Can you recap the fundamental difference between the
Speaker 1: deep web and the surface web and how we access
Speaker 1: it efficiently.
Speaker 2: Sure. The key distinction again is that deep web information
Speaker 2: is dynamically generated from databases in response to specific user requests.
Speaker 2: It's not like the static pre index pages of the
Speaker 2: surface web.
Speaker 1: Which is why Google struggles with it.
Speaker 2: Exactly, So, a specialized deep web crawler follows four main steps. First,
Speaker 2: it analyzes the query interface, the search box basically. Second,
Speaker 2: it intelligently assigns values to those query fields.
Speaker 1: Like filling in the form automatically kind.
Speaker 2: Of yeah, Third, it analyzes the response it gets back
Speaker 2: and navigates the results, maybe clicking through pages. And finally,
Speaker 2: it ranks the relevance of the information it finds.
Speaker 1: And that ranking is different too.
Speaker 2: Very different, because, unlike the surface web, the deep web
Speaker 2: lacks those direct hyperlinks between pages, so traditional link based
Speaker 2: quality assessments like PageRank don't really apply in the same way.
Speaker 1: And the source cover specific techniques and protocols for this, right.
Speaker 1: Can you give us a sense of what makes these
Speaker 1: crawlers so effective?
Speaker 2: Yeah, it gets pretty technical, but for identifying the search interfaces,
Speaker 2: they use sophisticated classification techniques like decision trees, random forest
Speaker 2: IRFA are mentioned. To understand the database structures. The schemas.
Speaker 2: There are mapping techniques like COMA and LSD, and for
Speaker 2: smartly assigning those query values. Methods like LGERM help with
Speaker 2: global aggregation and local scoring.
Speaker 1: And there are protocols too, like languages for talking to
Speaker 1: databases exactly.
Speaker 2: The source highlights various protocols proposed for deep web crawling,
Speaker 2: things like SRU, which is XML focused Z thirty nine
Speaker 2: point fifty, which is a client server protocol often used
Speaker 2: by libraries to search across multiple sources at.
Speaker 1: Once, like searching multiple university libraries from.
Speaker 2: One place, precisely that kind of thing. There's also OAIPMH
Speaker 2: for harvesting metadata PLQL, which combines XML and Information Retrieval
Speaker 2: host List protocol and sitemaps protocol. That last one uses
Speaker 2: an XML file. Webmasters can provide to tell search engines
Speaker 2: about URLs, especially useful for content generated by ajax or
Speaker 2: Flash that curlers might otherwise miss.
Speaker 1: So how do these specialized deep web search engines compared
Speaker 1: to the conventional ones we use every day? Google or bang.
Speaker 2: Well surface engines your Googles and bings. They cast a
Speaker 2: really wide net, right, they give you very general results.
Speaker 2: Lots of hits deep web engines, however, are far more
Speaker 2: efficient for specific often technical literature or data.
Speaker 1: They're more focused exactly.
Speaker 2: Their goal isn't just a long list of hits, but
Speaker 2: providing a right list of highly relevant information, quality and
Speaker 2: depth over just quantity.
Speaker 1: Can you mind a few?
Speaker 2: Sure? The source mentions some good examples Cyrus, which specializes
Speaker 2: in scientific, scholarly, technical, and medical data. Deep Dive, which
Speaker 2: is actually an online rental service for scientific and technical articles.
Speaker 2: You pay to access them for a period.
Speaker 1: Interesting model.
Speaker 2: Yeah, And Biznar, which focuses on business information and uses
Speaker 2: what's called federated search technology, meaning it queries multiple authoritative
Speaker 2: business databases simultaneously and combines the results for you. Very
Speaker 2: powerful for business research.
Speaker 1: Okay, So, with all this data floating around, both visible
Speaker 1: and hidden, what does this all mean when we talk
Speaker 1: about online identity? This section really makes you think about
Speaker 1: how our digital selves are constructed and perceived.
Speaker 2: It really does, and it raises a critical question, doesn't it?
Speaker 2: In a world just a wash with data, how do
Speaker 2: we balance the desire for information, sometimes the need for it,
Speaker 2: with our fundamental rights to privacy.
Speaker 1: It's a tough balance, it is.
Speaker 2: The Internet itself is nuanced, It's not inherently private or public.
Speaker 2: Our privacy depends so much on our own behavior and
Speaker 2: the specific services we choose to use.
Speaker 1: And this leads to harmful things like doxing attacks.
Speaker 2: Yes, doxing is a chilling example of that balance failing.
Speaker 2: It's when someone digs up and maliciously publishes personally identifiable
Speaker 2: information your address, phone number, family details to publicly shame,
Speaker 2: commit fraud, harass, or even directly harm someone horrible.
Speaker 1: And that's related to inference.
Speaker 2: Attacks closely related. Yeah, Inference attacks are about revealing unintended
Speaker 2: information about individuals or even hidden dark networks. These insights
Speaker 2: often come from analyzing our electronic data doubles or data
Speaker 2: doppel gangers.
Speaker 1: Our digital footprints.
Speaker 2: Essentially, Yes, profiles compile from all the data we shed online,
Speaker 2: sometimes data leaked by others about us without our knowledge.
Speaker 1: What tools are used to uncover these hidden connections and
Speaker 1: understand online social networks? How does that work well?
Speaker 2: Social Network analysis or SNA and its electronic counterpart ESNA
Speaker 2: are key here. These tools are designed specifically to capture, analyze,
Speaker 2: and visualize social networks.
Speaker 1: What can they show?
Speaker 2: They can reveal hidden relationships between people or groups, help
Speaker 2: understand network centrality, like who are the key influencers or
Speaker 2: connectors and identify different types of communities online.
Speaker 1: What assumptions do they make.
Speaker 2: They're generally built on core sociological assumptions that people are social,
Speaker 2: that we tend to prefer others like ourselves. That's hum awfully,
Speaker 2: birds of a feather exactly, but also that we connect
Speaker 2: across different groups that's heteropfully, and that social structures often
Speaker 2: have hierarchies.
Speaker 1: Are there specific software tools mentioned?
Speaker 2: Yes, Tools like Multego Radium are mentioned. It can apparently
Speaker 2: link something simple like an email address to a surprisingly
Speaker 2: wide range of other electronic info URLs, websites, visited phone numbers,
Speaker 2: even mapping out broader network structures wow and NOE Excela
Speaker 2: is often used specifically for mapping Twitter user networks and
Speaker 2: visualizing those complex social connections.
Speaker 1: How dynamic are these online networks? Do they stay static
Speaker 1: or are they constantly changing?
Speaker 2: Oh, They're incredibly dynamic. Members are constantly cycling in and out.
Speaker 2: Research shows large social networks typically have a few distinct parts.
Speaker 2: There's usually a giant component, a tightly connected core group.
Speaker 2: Then there's a middle region made up of smaller clusters,
Speaker 2: often formed around charismatic star individuals, and finally an outer
Speaker 2: periphery of isolated nodes or individual Do.
Speaker 1: These groups merge easily?
Speaker 2: What's particularly fascinating, according to the research sited, is that
Speaker 2: there seems to be a low likelihood of these isolated
Speaker 2: communities merge with the main core. Instead, those middle region
Speaker 2: clusters tend to either eventually merge with the main mass
Speaker 2: or they just disappear if the central star individual stops
Speaker 2: actively cultivating them.
Speaker 1: Interesting. That makes you wonder about the accuracy of these analyzes, though,
Speaker 1: What are the limits of these tools? Can they get
Speaker 1: it wrong?
Speaker 2: Oh? Absolutely, there are definite limits. The results can be
Speaker 2: significantly affected by things like the parameters the researcher sets
Speaker 2: for the data crawls, the unique quirks and limitations of
Speaker 2: the tools themselves.
Speaker 1: On human error, of course.
Speaker 2: Incorrect processes, misinterpretation of the data, or just flawed logic
Speaker 2: on the researcher's part. Plus, and this is key, the
Speaker 2: validity of any electronic identity exists on a continuum. Information
Speaker 2: is constantly changing online, so.
Speaker 1: A snapshot in time might not be accurate.
Speaker 2: Later exactly what's accurate at one moment may no longer
Speaker 2: be true. The next day or even the next hour.
Speaker 2: It's a snapshot, definitely not a permanent record.
Speaker 1: And what are the real world implications of this type
Speaker 1: of des data analysis? Does it affect us directly?
Speaker 2: One very direct and frankly startling implication mentioned is in
Speaker 2: the lending world. Apparently some lending entities now consider social
Speaker 2: network connections as data points when assessing loan suitability.
Speaker 1: Seriously, so, who you know online could affect your loan application?
Speaker 2: The suggestion is yes, if you have acquaintances online who
Speaker 2: happen to have poor credit scores, that connection could potentially
Speaker 2: and negatively affect your own loan eligibility. It really blurs
Speaker 2: the lines between your personal connections and your financial standing.
Speaker 1: That's quite something. Okay, let's shift now to the fascinating
Speaker 1: concept of becoming anonymous. Many people associate this name with
Speaker 1: a specific, often controversial group.
Speaker 2: And that's correct, But it's really important to understand anonymous
Speaker 2: not as a formal organization or a club, or even
Speaker 2: a movement with clear leaders or a fixed ideology.
Speaker 1: So what is it? Then?
Speaker 2: The source defines it more like people who travel a
Speaker 2: short distance together, united temporarily by common goals or dislay
Speaker 2: its infrastructure is remarkably decentralized and incredibly adaptable.
Speaker 1: How did it operate?
Speaker 2: It leverages existing Internet facilities, social networks, IRC, internet, really
Speaker 2: chat imageboards like four Chan, and they're known for being
Speaker 2: incredibly quick to shift to new platforms if a previous
Speaker 2: one gets compromised or shut down.
Speaker 1: And they gained global notoriety pretty quickly, didn't they background
Speaker 1: twenty ten.
Speaker 2: Absolutely, they really burst onto the global scene by orchestrating
Speaker 2: those online uprisings in support of WikiLeaks in twenty ten,
Speaker 2: and then notably they claim to have infiltrated natosystems in
Speaker 2: twenty eleven.
Speaker 1: What are their defining characteristics according to the research.
Speaker 2: A constantly changing membership for one, an increasing politicization over time,
Speaker 2: engagement and actions that are often illegal, and that distinct
Speaker 2: fluid network structure we just talked about.
Speaker 1: The iconic guy Fox mask is practically synonymous with anonymous.
Speaker 1: Now what's its deeper significance beyond just being a disguise?
Speaker 1: Where did that come from?
Speaker 2: Well? The ask was hugely popularized by the two thousand
Speaker 2: and six movie V for Vendetta. Of course, symbolically it's
Speaker 2: seen as challenging cultural assumptions. It represents a kind of
Speaker 2: paradoxical non identity, a non identity yeah, rejecting clear pet
Speaker 2: either alternatives, it serves as a powerful strategy, arguably to
Speaker 2: protect the individual self in an age of pervasive surveillance.
Speaker 2: There's also a carnivalesque element to it that the source discusses,
Speaker 2: how so, in the sense that laughter becomes both protests
Speaker 2: and acceptance. Social distinctions are temporarily suspended, and the mask
Speaker 2: itself symbolizes an escape from a fixed social personality or identity.
Speaker 1: That's a powerful symbol, especially when you can trast the
Speaker 1: movie's character V with the actual historical Guy Fox.
Speaker 2: Indeed, the historical Guy Fox, as you know, was caught,
Speaker 2: tortured and his plot failed miserably. In stark contrast, V
Speaker 2: in the movie successfully blows up Parliament, and crucially with
Speaker 2: collective participation from the masked masses. This emphasizes that for
Speaker 2: anonymous the idea itself, freedom, anti authoritarianism, whatever it might
Speaker 2: be at the moment, matters more than the specific individual
Speaker 2: behind the mask.
Speaker 1: There's a weird link to an Internet memes.
Speaker 2: Yeah, it's an odd cultural footnote, but the source mentions
Speaker 2: how the epic fail guy meme in a strange twist
Speaker 2: of Internet culture, might have even helped set the stage
Speaker 2: for Anonymous's adoption of the mask. It apparently created a
Speaker 2: kind of seamless cognitive link back to the historical figure
Speaker 2: who was, after all, famous for an epic fail.
Speaker 1: Huh, strange connections. Okay, finally, let's turn our attention to
Speaker 1: the cutting edge of web crawling itself. This section focuses
Speaker 1: on optimizing how we access and retrieve all this hidden
Speaker 1: information we've been talking about, right.
Speaker 2: And this really underscores how continuous innovation in crawling technologies
Speaker 2: is absolutely essential. We need it to keep pace with
Speaker 2: the Web's evolving structure and to effectively surface all this
Speaker 2: vast hidden data. It's fundamentally about making the deep web
Speaker 2: truly searchable and useful.
Speaker 1: So what's the main challenge again, that traditional search engines
Speaker 1: face when trying to index this deep web? Why can't
Speaker 1: Google just do it?
Speaker 2: The fundamental problem is that conventional search engines are primarily
Speaker 2: designed to index static, linked pages. They really struggle to
Speaker 2: efficiently crawl the massive deep Web because, as we've said,
Speaker 2: its pages are dynamic, generated on the fly, and they
Speaker 2: are often hidden behind restricted search interfaces like log in
Speaker 2: pages or complex forms.
Speaker 1: Yeah, that makes it expensive.
Speaker 2: Exactly, It significantly increases the costs associated with accessing the data,
Speaker 2: storing it, and communicating it. The proposed solution discussed here
Speaker 2: is the idea of vertical search engines cerbical, meaning domain
Speaker 2: specific engines designed to provide much better quality search results,
Speaker 2: but only within a specific area of deep web content,
Speaker 2: narrowing the focus to say, academic papers, or job listings
Speaker 2: or flight information.
Speaker 1: And the core objective of these new systems is to
Speaker 1: dramatically reduce those costs while providing better, more relevant results.
Speaker 2: Precisely, that's the goal. They achieve this by using techniques
Speaker 2: like parallel computing, doing many things at once, distributing the
Speaker 2: hardware and software load, and employing more efficient indexing techniques
Speaker 2: like automatic segmentation to get high precision results.
Speaker 1: Is there a way to measure the cost?
Speaker 2: The source even provides a cost calculation model for querying
Speaker 2: a database. It basically looks at the number of records
Speaker 2: matched by a query versus the maximum number displayed per
Speaker 2: result page cost gidb eto like dluddbk as the formula.
Speaker 1: Puts it, and the results for promising.
Speaker 2: Yeah, experimental results have consistently demonstrated significant reductions in communication costs,
Speaker 2: access costs, storage costs, and computational costs. It's really about
Speaker 2: being smarter, not just bigger or faster, in how we
Speaker 2: approach searching this hidden data.
Speaker 1: What about designing a whole new architecture for deep web crawlers?
Speaker 1: Are their efforts to fundamentally rethink how they work to
Speaker 1: uncover even more data?
Speaker 2: Yes, because even existing deep web crawlers still leave a
Speaker 2: large volume of data undiscovered. So one chapter poses a
Speaker 2: novel architecture based on something called the QIIEP specification KEYIP.
Speaker 1: What does that improve?
Speaker 2: Think of it as a much smarter way for the
Speaker 2: crawler to interact with web forms. It includes improvements like
Speaker 2: enhanced form filling capabilities, more automatic query selections so it
Speaker 2: knows what to search for, and minimizing errors when it
Speaker 2: follows links or forwards pages.
Speaker 1: Does it involve different parts working together?
Speaker 2: It does. It lists key modules within the architecture, things
Speaker 2: like a page fetcher, a page analyzer, a form id manager,
Speaker 2: the QIP server itself, a form submitter, link extractor, link ranker,
Speaker 2: and so on. They all work in concert.
Speaker 1: Can you simplify the process?
Speaker 2: Basically, it starts with initial page analysis and filtering links.
Speaker 2: Then it identifies the search interfaces, uses this QIAP server
Speaker 2: to correlate the form fields, intelligently submits the filled forms,
Speaker 2: and then crawls, ranks and stores the dynamic content that
Speaker 2: gets generated.
Speaker 1: And how effective is this new architecture in actually finding
Speaker 1: the hidden data? Does it work better?
Speaker 2: It's a significantly high harvest ratio for focused domains, for instance,
Speaker 2: achieving nearly eighty percent harvests for job and book related
Speaker 2: searches and over fifty percent for autodomains.
Speaker 1: So much better than previous methods.
Speaker 2: Yes, this indicates a substantial improvement over existing approaches. The
Speaker 2: overall benefits are clear better performance, reduced costs for deep
Speaker 2: web searching using a domain specific formula, and highly effective
Speaker 2: link generation. It boasts over forty percent effective links with
Speaker 2: forms extraction per loop, meaning it's far more efficient at
Speaker 2: pulling out that dynamic information we're looking for.
Speaker 1: Okay, Our final point in is deep dive touches on
Speaker 1: how search engines, both surface and deep act as a
Speaker 1: kind of backbone for information extraction across so many fields
Speaker 1: in our modern ICT world.
Speaker 2: Absolutely information and communication technology ICT and search engines underpinning
Speaker 2: it play just a monumental role in human development across
Speaker 2: incredibly vast application areas like what well, education obviously, the
Speaker 2: business environment, human resources, job information, searching, e commerce, online banking,
Speaker 2: database related information extraction, in general, health information, e government services.
Speaker 1: The list goes on, and the deep web information is
Speaker 1: particularly crucial here.
Speaker 2: Often yes, because its dynamic pages can offer that right
Speaker 2: list of highly relevant specific information rather than just a
Speaker 2: long list of general hits from the surface web. And
Speaker 2: crucially it often provides access to incredibly authoritative primary source sites.
Speaker 1: And just to recap, there are different types of web
Speaker 1: crawlers tailored for different needs, reflecting the web's diverse structure exactly.
Speaker 2: You've got the simple crawler, which is just a single process,
Speaker 2: the parallel crawler using multi threading for faster downloads, focused
Speaker 2: crawlers designed to zero in on specific topics or domains,
Speaker 2: incremental crawlers which are smart enough to only go back
Speaker 2: and update pages that.
Speaker 1: Have actually changed, shaving resources right.
Speaker 2: And then the hidden or deep crawlers we've been focusing
Speaker 2: on specifically designed for those dynamic pages and the vast
Speaker 2: amounts of online data hidden behind forms.
Speaker 1: Can you just highlight the key differences one last time
Speaker 1: between the surface web and the deep web that these
Speaker 1: various crawlers NAVI just to really solidify our understanding.
Speaker 2: Certainly so. The surface web, mostly static linked pages, generally
Speaker 2: offers broad less specialized content. It doesn't publish results through
Speaker 2: direct database queries, its content often comes from less professional
Speaker 2: or structured sources, and it represents just a fraction of
Speaker 2: the overall unstructured content online.
Speaker 1: Okay, and the deep web.
Speaker 2: The deep web dynamic holds an enormous amount of online data.
Speaker 2: It requires more resources and processing power to access, but
Speaker 2: in return, it offers narrower, much deeper, often higher quality content.
Speaker 2: It publishes results through direct queries to databases, and frequently
Speaker 2: contains highly professional, authoritative material. It's truly the hidden bulk
Speaker 2: of the Internet.
Speaker 1: Iceberg and we mentioned some specific deep web search engines
Speaker 1: tailored for these needs.
Speaker 2: Yes, just reinforcing the point cirus focusing on science, scholarly, technical,
Speaker 2: and medical data. Deep Dive the rental service for research articles,
Speaker 2: and bisnard the business focus engine, using that federated search
Speaker 2: technology to combine results from multiple authoritative business collections simultaneously.
Speaker 2: Tools designed for specific deep dives.
Speaker 1: What an incredible deep dive that was into the deepened
Speaker 1: the dark Web. Yeah, we've truly unpacked a lot today,
Speaker 1: everything from cybercrimes, unsettling psychological roots you know that dark
Speaker 1: triad stuff.
Speaker 2: Yeah, and the fascinating complex social dynamics of groups like Anonymous.
Speaker 1: Right all the way to the cutting edge technology that
Speaker 1: helps us navigate and even harness these hidden digital landscapes,
Speaker 1: the crawlers, the analysis.
Speaker 2: We've really seen how the Internet is just this complex,
Speaker 2: constantly evolving entity, hasn't it always blurring the lines between
Speaker 2: public and private, visible and hidden?
Speaker 1: Definitely?
Speaker 2: And how continuous innovation in data extraction and crawling tech
Speaker 2: is just absolutely essential to surface and harness its vast,
Speaker 2: often hidden information for everything from academic research to business intelligence.
Speaker 1: Well, hopefully this deep dive has given you, our listener,
Speaker 1: a bit of a shortcut to being truly well informed
Speaker 1: on this. Maybe offer those aha moments without the usual
Speaker 1: information overload.
Speaker 2: Yeah, you should now have a more nuanced understanding, hopefully,
Speaker 2: of the powerful forces shaping our digital interactions and the
Speaker 2: sheer amount of hidden data that influences so much of
Speaker 2: our world, often thought us even realizing it.
Speaker 1: So here's a final provocative thought to leave you with
Speaker 1: as you go about your day.
Speaker 2: Consider this. As the Web continues its relentless evolution and
Speaker 2: more and more of our lives move online, how will
Speaker 2: the very definitions of public and private, visible and hidden
Speaker 2: continue to shift and change, And.
Speaker 1: Perhaps more importantly, what responsibilities do we all have as users,
Speaker 1: as developers, as citizens in consciously shaping that complex, interconnected future.