Social media platforms are experiencing a growing presence of AI-Generated Texts (AIGTs). However, the misuse of AIGTs could have profound implications for public opinion, such as spreading misinformation and manipulating narratives. Despite its importance, a systematic study to assess the prevalence of AIGTs on social media is still lacking. To address this gap, this paper aims to quantify, monitor, and analyze the AIGTs on online social media platforms. We first collect a dataset (SM-D) with around 2.4M posts from 3 major social media platforms: Medium, Quora, and Reddit. Then, we construct a diverse dataset (AIGTBench) to train and evaluate AIGT detectors. AIGTBench combines popular open-source datasets and our AIGT datasets generated from social media texts by 12 LLMs, serving as a benchmark for evaluating mainstream detectors. With this setup, we identify the best-performing detector (OSM-Det). We then apply OSM-Det to SM-D to track AIGTs over time and observe different trends of AI Attribution Rate (AAR) across social media platforms from January 2022 to October 2024. Specifically, Medium and Quora exhibit marked increases in AAR, rising from 1.77% to 37.03% and 2.06% to 38.95%, respectively. In contrast, Reddit shows slower growth, with AAR increasing from 1.31% to 2.45% over the same period. Our further analysis indicates that AIGTs differ from human-written texts across several dimensions, including linguistic patterns, topic distributions, engagement levels, and the follower distribution of authors. We envision our analysis and findings on AIGTs in social media can shed light on future research in this domain.

This article was featured in the One Story to Read Today newsletter. Every day or two for the past seven months, I've received a "personalized" email containing a bunch of recent, user-generated questions from the website Quora. "I caught my son playing his Xbox at 12:00 in the morning on a school night. As a result, I broke his console and now he won't talk to me. How can I tell him that it is his fault?"

Following Sam Altman's rollercoaster of a return as OpenAI's CEO, the company announced that it will now include Microsoft as a non-voting observer on its board. The question remains as to why the firm's largest investor wasn't on its board in the first place, but this seems to be somewhat addressed for now, at least. Altman is joined by co-founder Greg Brockman who resumes his role as President, whereas Mira Murati, who very briefly served as interim CEO throughout the drama, will return to her role as CTO. The announcement also confirms a new board consisting of former Salesforce CEO Bret Taylor (chair), former Clinton Treasury Secretary Larry Summers, and original member Adam D'Angelo, who is also Quora's co-founder and CEO. It was earlier rumored that Altman's exit was partly influenced by D'Angelo's seeming conflict of interest, as OpenAI was developing a potential competitor to Quora's Poe service -- the latter offers OpenAI's ChatGPT and GPT-4, along with several other text-generating AI models.

Deep generative modeling of natural languages has achieved many successes, such as producing fluent sentences and translating from one language into another. However, the development of generative modeling techniques for paraphrase generation still lags behind largely due to the challenges in addressing the complex conflicts between expression diversity and semantic preservation. This paper proposes to generate diverse and high-quality paraphrases by exploiting the pre-trained models with instance-dependent prompts. To learn generalizable prompts, we assume that the number of abstract transforming patterns of paraphrase generation (governed by prompts) is finite and usually not large. Therefore, we present vector-quantized prompts as the cues to control the generation of pre-trained models. Extensive experiments demonstrate that the proposed method achieves new state-of-art results on three benchmark datasets, including Quora, Wikianswers, and MSCOCO. We will release all the code upon acceptance.

AI-generated text has proliferated across various online platforms, offering both transformative prospects and posing significant risks related to misinformation and manipulation. It incorporates real AI-generate text from popular social media platforms like Zhihu and Quora. Unlike existing benchmarks, SAID deals with content that reflects the sophisticated strategies employed by real AI users on the Internet which may evade detection or gain visibility, providing a more realistic and challenging evaluation landscape. A notable finding of our study, based on the Zhihu dataset, reveals that annotators can distinguish between AI-generated and human-generated texts with an average accuracy rate of 96.5%. Furthermore, we present a new user-oriented AI-text detection challenge focusing on the practicality and effectiveness of identifying AI-generated text based on user information and multiple responses. The experimental results demonstrate that conducting detection tasks on actual social media platforms proves to be more challenging compared to traditional simulated AI-text detection, resulting in a decreased accuracy. On the other hand, user-oriented AI-generated text detection significantly improve the accuracy of detection. The advent of AI-generated text has had a profound impact on numerous sectors, including social media platforms. On one side, AI-generated responses enable automation, personalization, and scaling of content creation, thereby revolutionizing how information is disseminated and consumed. Addressing the abuse and malicious use of AI has led to significant research efforts in the field of AI-generated text detection. A range of approaches have been explored, including but not limited to machine learning algorithms Guo et al. (2023); Solaiman et al. (2019), text-based features analysis Mitchell et al. (2023); Tulchinskii et al. (2023); Mitchell et al. (2023), and positive unlabeled techniques Tian et al. (2023).

Yesterday, OpenAI unveiled its new GPT-4 model and competitor Anthropic unveiled its own ChatGPT competitor, Claude. Parallelly, Quora announced that its chatbot app Poe will now have a paid tier that will let you ask questions to bots powered by these models. Poe subscriptions will set you back $19.99 per month or $199.99 per year, and you can only buy it at the moment from your iOS or Apple Silicon-powered Mac. The company is working on making the paid plan available to purchase on the web. Quora first launched Poe last December as a closed beta and later opened it up to all iOS users last month.

Further generations of humans--or robots--might one day look back on this week as the tipping point in the way that computers and people interact. On Monday, CEO Sundar Pichai announced Google's new chatbot, dubbed Bard, based on its previously disclosed AI bot LaMDA. A day later, Microsoft unveiled a new version of search engine Bing, powered by OpenAI's breakaway hit ChatGPT. But I had already tumbled into that rabbit hole after pondering a less-heralded beta product soft-launched last December and opened to the public a week ago. It is a chatbot called Poe, produced by, of all companies, Quora, a 14-year-old social network that helps users find answers to questions by tapping the knowledge of other users. Like Quora itself, you type in your question and wait for the answer.

Q&A platform Quora has opened up public access to its new AI chatbot app, Poe, which lets users ask questions and get answers from a range of AI chatbots, including those from ChatGPT maker, OpenAI, and other companies like Anthropic. Beyond allowing users to experiment with new AI technologies, Poe's content will ultimately help to evolve Quora itself, the company says. Quora had first announced Poe's mobile app in December, but at the time, it required an invite to try it out. With the public launch on Friday, anyone can now use Poe's app. For now, it's available only to iOS users, but Quora says the service will arrive on other platforms in a few months.

What is the difference between artificial intelligence and machine learning, and when would you use one over the other?

This position can be performed remotely from anywhere in the world, regardless of any location that might be specified above.] The vast majority of human knowledge is still not on the internet. Most of it is trapped in the form of experience in people's heads, or buried in books and papers that only experts can access. More than a billion people use the internet, yet only a tiny fraction contribute their knowledge to it. We want to democratize access to knowledge of all kinds -- from politics to painting, cooking to coding, etymology to experiences -- so if someone out there knows something, anyone else can learn it.