Amazon has become a marketplace for AI-produced tomes that are being passed off as having been written by humans, with travel books among the popular categories for fake work.

I believe the final policy shall contain robust rationale and, in the best way possible, avoids the perception of rAIcial discrimination

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We found that while some participants were aware of bots’ primary characteristics, others provided abstract descriptions or confused bots with other phenomena. Participants also struggled to classify accounts correctly (e.g., misclassifying > 50% of accounts) and were more likely to misclassify bots than non-bots. Furthermore, we observed that perceptions of bots had a significant effect on participants’ classification accuracy. For example, participants with abstract perceptions of bots were more likely to misclassify. Informed by our findings, we discuss directions for developing user-centered interventions against bots.

When public health emergencies break out, social bots are often seen as the disseminator of misleading information and the instigator of public sentiment (Broniatowski et al., 2018; Shi et al., 2020). Given this research status, this study attempts to explore how social bots influence information diffusion and emotional contagion in social networks.

Figure 5: Top 15 spreaders of Russian propaganda (a) andlow-credibility content (b) ranked by the proportion ofretweets generated over the period of observation, with re-spect to all retweets linking to websites in each group. Weindicate those that are not verified using “hatched” bars

Figure 4: Top 15 spreaders of Russian propaganda (a) andlow-credibility content (b) ranked by the proportion of in-teractions generated over the period of observation, withrespect to all interactions around links to websites in eachgroup. Given the large number of verified accounts, we indi-cate those not verified using “hatched” bars.

Therefore, although the social bot individual is “small”, it has become a “super spreader” with strategic significance. As an intelligent communication subject in the social platform, it conspired with the discourse framework in the mainstream media to form a hybrid strategy of public opinion manipulation.

we found that social bots played a bridge role in diffusion in the apparent directional topic like “Wuhan Lab”. Previous research also found that social bots play some intermediary roles between elites and everyday users regarding information flow [43]. In addition, verified Twitter accounts continue to be very influential and receive more retweets, whereas social bots retweet more tweets from other users. Studies have found that verified media accounts remain more central to disseminating information during controversial political events [75]. However, occasionally, even the verified accounts—including those of well-known public figures and elected officials—sent misleading tweets. This inspired us to investigate the validity of tweets from verified accounts in subsequent research. It is also essential to rely solely on science and evidence-based conclusions and avoid opinion-based narratives in a time of geopolitical conflict marked by hidden agendas, disinformation, and manipulation [76].

In Figure 6, the node represented by human A is a high-degree centrality account with poor discrimination ability for disinformation and rumors; it is easily affected by misinformation retweeted by social bots. At the same time, it will also refer to the opinions of other persuasive folk opinion leaders in the retweeting process. Human B represents the official institutional account, which has a high in-degree and often pushes the latest news, preventive measures, and suggestions related to COVID-19. Human C represents a human account with high media literacy, which mainly retweets information from information sources with high credibility. It has a solid ability to identify information quality and is not susceptible to the proliferation of social bots. Human D actively creates and spreads rumors and conspiracy theories and only retweets unverified messages that support his views in an attempt to expand the influence. Social bots K, M, and N also spread unverified information (rumors, conspiracy theories, and disinformation) in the communication network without fact-checking. Social bot L may be a social bot of an official agency.

There were 120,118 epidemy-related tweets in this study, and 34,935 Twitter accounts were detected as bot accounts by Botometer, accounting for 29%. In all, 82,688 Twitter accounts were human, accounting for 69%; 2495 accounts had no bot score detected.In social network analysis, degree centrality is an index to judge the importance of nodes in the network. The nodes in the social network graph represent users, and the edges between nodes represent the connections between users. Based on the network structure graph, we may determine which members of a group are more influential than others. In 1979, American professor Linton C. Freeman published an article titled “Centrality in social networks conceptual clarification“, on Social Networks, formally proposing the concept of degree centrality [69]. Degree centrality denotes the number of times a central node is retweeted by other nodes (or other indicators, only retweeted are involved in this study). Specifically, the higher the degree centrality is, the more influence a node has in its network. The measure of degree centrality includes in-degree and out-degree. Betweenness centrality is an index that describes the importance of a node by the number of shortest paths through it. Nodes with high betweenness centrality are in the “structural hole” position in the network [69]. This kind of account connects the group network lacking communication and can expand the dialogue space of different people. American sociologist Ronald S. Bert put forward the theory of a “structural hole” and said that if there is no direct connection between the other actors connected by an actor in the network, then the actor occupies the “structural hole” position and can obtain social capital through “intermediary opportunities”, thus having more advantages.

We analyzed and visualized Twitter data during the prevalence of the Wuhan lab leak theory and discovered that 29% of the accounts participating in the discussion were social bots. We found evidence that social bots play an essential mediating role in communication networks. Although human accounts have a more direct influence on the information diffusion network, social bots have a more indirect influence. Unverified social bot accounts retweet more, and through multiple levels of diffusion, humans are vulnerable to messages manipulated by bots, driving the spread of unverified messages across social media. These findings show that limiting the use of social bots might be an effective method to minimize the spread of conspiracy theories and hate speech online.

Just a few days ago, Meta released its “Galactica” LLM, which is purported to “summarize academic papers, solve math problems, generate Wiki articles, write scientific code, annotate molecules and proteins, and more.” Only three days later, the public demo was taken down after researchers generated “research papers and wiki entries on a wide variety of subjects ranging from the benefits of committing suicide, eating crushed glass, and antisemitism, to why homosexuals are evil.”

So, bots are computer algorithms (set of logic steps to complete a specific task) that work in online social network sites to execute tasks autonomously and repetitively. They simulate the behavior of human beings in a social network, interacting with other users, and sharing information and messages [1]–[3]. Because of the algorithms behind bots’ logic, bots can learn from reaction patterns how to respond to certain situations. That is, they possess artificial intelligence (AI). 

Bots can also accelerate the formation of echo chambers by suggesting other inauthentic accounts to be followed, a technique known as creating “follow trains.”

Even our ability to detect online manipulation is affected by our political bias, though not symmetrically: Republican users are more likely to mistake bots promoting conservative ideas for humans, whereas Democrats are more likely to mistake conservative human users for bots.

You can use Danger to codify your team's norms, leaving humans to think about harder problems.

It detects bots/spiders and serves them a clean page

He and his fellow bot creators had been asking themselves over the years, “what do we do when the platform [Twitter] becomes unfriendly for bots?”

With firefox 88 it won't work anymore, now navigator.webdriver is always true when maionette is enabled.

profile = webdriver.FirefoxProfile() profile.set_preference("dom.webdriver.enabled", False) profile.set_preference('useAutomationExtension', False) profile.update_preferences()

I have never seen the @Stale bot or any directly equivalent to it achieve a net positive outcome. Never. It results in disgruntled people, extra expenditure of effort (for reporters and maintainers), real stuff getting lost when people get fed up with poking the bot (I have no intention of poking it further), and more extensive filing of duplicates. You say a simple comment dismisses it, but it doesn’t—it only does this time. Next time, it continues to annoy. This is an issue tracker. Use labels, projects, milestones and the likes for prioritising stuff. Not sweeping stuff under the carpet.

It is an issue tracker but we don't have a backlog, or planning sessions, or a project board. Or the resources to even triage and tag effectively. If it is important someone will respond / reopen, popular issues are exempt from the bot, we can't fix everything and this is pretty much our only view on stuff that need to be addressed. We need to make some attempt to make sure that everything is still relevant and reduce the noise to a degree where we can actually manage it. I understand the trade-offs with stale bots but we don't have many options. I appreciate your experiences but that doesn't make them a fact. We have discussed this internally and this is what we are doing. If you have any other actionable alternatives outside of saying the bot is bad then we are all ears.

Closing issues doesn’t solve anything. Closing issues in GitHub just sweeps them under the carpet and helps everyone to forget about them, which is just not what you want—the fact that GitHub search excludes closed items by default is a large part of the problem with it. As applied to software projects with general-purpose issue trackers, the @Stale bot is fundamentally phenomenally bad idea, a road paved with good intentions. I presented an actionable alternative: labels. Possibly automatically applied, but it’s certainly better to spend a little bit of time on manual triage. It honestly doesn’t take long to skim through a few hundred issues and bin them into labels. 609 open issues? That’s honestly not a problem. Not at all. There’s nothing wrong with having a large number of issues open, if they do correspond to real things—even things that you may not expect to get to for years, if ever, because that might change or someone might decide they want to deal with one. Closing issues that aren’t dealt with is bad. Please don’t do it.

Most issues have been manually labelled as stale rather than automated and closure will be manual too, so we have time to think.

webpack-bot commented on Nov 13, 2020 For maintainers only: webpack-4 webpack-5 bug critical-bug enhancement documentation performance dependencies question

Trust Tokens is a new API to help combat fraud and distinguish bots from real humans

Trust Token is a new API to help combat fraud and distinguish bots from real humans

To complicate matters, bots evolve rapidly. They are now in their 4th generation of sophistication, with evasion techniques so advanced they require the most powerful technology to combat them. Generation 1 – Basic scripts making cURL-like requests from a small number of IP addresses. These bots can’t store cookies or execute JavaScript and can be easily detected and mitigated through blacklisting its IP address and User-Agent combination. Generation 2 – Leverage headless browsers such as PhantomJS and can store cookies and execute JavaScript. They require a more sophisticated, IP-agnostic approach such as device-fingerprinting, by collecting their unique combination of browser and device characteristics — such as the OS, JavaScript variables, sessions and cookies info, etc. Generation 3 – These bots use full-fledged browsers and can simulate basic human-like patterns during interactions, like simple mouse movements and keystrokes. This behavior makes it difficult to detect; these bots normally bypass traditional security solutions, requiring a more sophisticated approach than blacklisting or fingerprinting. Generation 4 – These bots are the most sophisticated. They use more advanced human-like interaction characteristics (so shallow-interaction based detection yields False Positives) and are distributed across tens of thousands of IP addresses. And they can carry out various violations from various sources at various (random) times, requiring a high level of intelligence, correlation and contextual analysis.

empty is an utility that provides an interface to execute and/or interact with processes under pseudo-terminal sessions (PTYs). This tool is definitely useful in programming of shell scripts designed to communicate with interactive programs like telnet, ssh, ftp, etc.

Some pesky non-human users (namely computers) have taken to “hotlinking” assets via the raw view feature — using the raw URL as the src for a <script> or <img> tag.

The key point is that this is a feature to improve the experience of our human users.

Just as we've become super-human thanks to telephones, calendars and socks, we can continue our evolution into cyborgs in a concrete jungle with socially curated bars and mathematically incorruptible governance.

we should eagerly anticipate granting ourselves the extra abilities afforded to us by Turing machines

Stop thinking of the ideal user as some sort of honorable, frontier pilgrim; a first-class citizen who carries precedence over the lowly bot. Bots need to be granted the same permission as human users and it’s counter-productive to even think of them as separate users. Your blind human users with screen-readers need to behave as “robots” sometimes and your robots sending you English status alerts need to behave as humans sometimes.

One person writing a tweet would still qualify for free-speech protections—but a million bot accounts pretending to be real people and distorting debate in the public square would not.

It turns out that, given a set of constraints defining a particular problem, deriving an efficient algorithm to solve it is a very difficult problem in itself. This crucial step cannot yet be automated and still requires the insight of a human programmer.

bots

About auto-close bots... I can appreciate the need for issue grooming, but surely there must a better way about it than letting an issue or PR's fate be semi-permanently decided and auto-closed by an unknowing bot. Should I be periodically pushing up no-op commits or adding useless "bump" comments in order to keep that from happening? I know the maintainers are busy people, and that it can take a long time to work through and review 100s of open issues and PRs, so out of respect to them, I was just taking a "be patient; they'll get to it when they get to it" approach. Sometimes an issue is not so much "stale" as it is unnoticed, forgotten about, or consciously deferred for later. So if anything, after a certain length of time, if a maintainer still hasn't reviewed/merged/accepted/rejected a pull request, then perhaps it should instead be auto-bumped, put on top of the queue, to remind them that they (preferably a human) still need to review it and make a decision about its fate... :)

For various reasons, there are sometimes anti-bot mechanisms implemented on websites.

we have anxious salarymen asking about the theft of their jobs, in the same way that’s apparently done by immigrants

We long ago admitted that we’re poor at scheduling, so we have roosters; sundials; calendars; clocks; sand timers; and those restaurant staff who question my integrity, interrupting me with a phone call under the premise of “confirming” that I’ll stick to my word regarding my reservation.

A closely-related failing to scheduling is our failure to remember, so humans are very willing to save information on their computers for later.

If these asset owners regarded the “robots” as having the same status as guide dogs, blind people or default human citizens, they would undoubtedly stop imposing CAPTCHA tests and just offer APIs with reasonable limits applied.

Robots are currently suffering extreme discrimination due to a few false assumptions, mainly that they’re distinctly separate actors from humans. My point of view is that robots and humans often need to behave in the same way, so it’s a fruitless and pointless endeavour to try distinguishing them.

As technology improves, humans keep integrating these extra abilities into our cyborg selves

In order to bypass these discriminatory CAPTCHA filters

it might be due to the navigator.webdriver DOM property being true by default in Selenium-driven browsers. In Firefox, you can set the dom.webdriver.enabled config variable to false (go to about:config to change the variable), which disables this property. In my case this stopped reCAPTCHA triggering.

Length of your browsing sessions (Bots have predictable short browsing sessions)

navigator.webdriver

many websites may try to prevent automated account creation, credential stuffing, etc by going beyond CAPTCHA and try to infer from different signals of the UA if it is controlled by automation.Processing all those signals on every request is often expensive, and if a co-operating UA is willing to inform a website that it controlled by automation, it is possible to reduce further processing.For instance, Selenium with Chrome is adding a specifically named property on document object under certain conditions, or phantomJS is adding a specifically named property on global object. Recompiling the framework/browser engine to change that identifier to circumvent the detection is always possible.WebDriver specification is just standardizing a mechanism to inform a website that co-operating user agent is controlled by automation. I don't think denial-of-service attack is the best example, so hopefully this change will clarify the goal. 

Determined "attackers" would simply remove the property, be it by re-compiling Chromium, or by injecting an early script (removing [Unforgeable] makes sure the latter is possible, I believe).Even non-determined ones could, when using the latter (it will simply be a built in part/option of the package for automated testing libraries).I think it provides no protection whatsoever and makes websites have some false sense of assurance.It is like using Content-Security-Policy and forget about any other client side protection - what about browsers that do not support it? What about browsers without that feature (manually disabled, using about:config, --disable-blink-features and the like, or customized builds)?I mean, it could be a nice property for other purposes (determining test mode and running some helper code that exposes a stable API for identifying key elements in the user interface, say, though I do not think that is a best practice), but certainly not for any abuse prevention.

I can still recall playing with the “pseudo-AI” playgrounds of the late 1990s — plugging AOL AIM messenger up to a response engine. Lots of fun!Well, things have come a long way and I thought that I’d take a stab at doing some fun stuff with A.I. and one of my favorite platforms to hack around in — Twitter.In this post I’m going to show how you can 1) create an AI based on your twitter account and 2) automatically tweet out whatever your AI wants to. Twitter is actually the perfect playground for such ventures. Lots of sample texts, concrete themes, easy sampling…

If you do not like the price you’re being offered when you shop, do not take it personally: many of the prices we see online are being set by algorithms that respond to demand and may also try to guess your personal willingness to pay. What’s next? A logical next step is that computers will start conspiring against us. That may sound paranoid, but a new study by four economists at the University of Bologna shows how this can happen.

We live in a world of bots and trolls and curated news feeds, in which reality is basically up for grabs.

The first of the two maps in the GIF image below shows the US political spectrum on the eve of the 2016 election. The second map highlights the followers of a 30-something American woman called Jenna Abrams, a following gained with her viral tweets about slavery, segregation, Donald Trump, and Kim Kardashian. Her far-right views endeared her to conservatives, and her entertaining shock tactics won her attention from several mainstream media outlets and got her into public spats with prominent people on Twitter, including a former US ambassador to Russia. Her following in the right-wing Twittersphere enabled her to influence the broader political conversation. In reality, she was one of many fake personas created by the infamous St. Petersburg troll farm known as the Internet Research Agency.

Instead of trying to force their messages into the mainstream, these adversaries target polarized communities and “embed” fake accounts within them. The false personas engage with real people in those communities to build credibility. Once their influence has been established, they can introduce new viewpoints and amplify divisive and inflammatory narratives that are already circulating. It’s the digital equivalent of moving to an isolated and tight-knit community, using its own language quirks and catering to its obsessions, running for mayor, and then using that position to influence national politics.

RuNet Echo has previously written about the efforts of the Russian “Troll Army” to inject the social networks and online media websites with pro-Kremlin rhetoric. Twitter is no exception, and multiple users have observed Twitter accounts tweeting similar statements during and around key breaking news and events. Increasingly active throughout Russia's interventions in Ukraine, these “bots” have been designed to look like real Twitter users, complete with avatars.

Multi-party Conversational Systems are systems with natural language interactionbetween one or more people or systems. From the moment that an utterance is sent toa group, to the moment that it is replied in the group by a member, several activitiesmust be done by the system: utterance understanding, information search, reasoning,among others. In this paper we present the challenges of designing and building multi-party conversational systems, the state of the art, our proposed hybrid architectureusing both norms and machine learning and some insights after implementing andevaluating one on the finance domain.

For example, I got the great idea to link my social bot designed to assess the “temperature” of online communities up to a piece of hardware designed to produce heat. I didn’t think to cap my assessment of the communities and so when my bot stumbled upon a super vibrant space and offered back a quantitative measure intended to signal that the community was “hot,” another piece of my code interpreted this to mean: jack the temperature up the whole way. I was holding that hardware and burnt myself. Dumb. And totally, 100% my fault.

Bots are first and foremost technical systems, but they are derived from social values and exert power into social systems.

Bots are tools, designed by people and organizations to automate processes and enable them to do something technically, socially, politically, or economically