The death of Martin Parr, the photographer whose work chronicled the rituals and customs of British life, was front-page news in France and his life and work were celebrated as far afield as the US and Japan.
If his native England had to shake off…

The death of Martin Parr, the photographer whose work chronicled the rituals and customs of British life, was front-page news in France and his life and work were celebrated as far afield as the US and Japan.
If his native England had to shake off…

Kumail JafferLocal Democracy Reporting Service
Getty ImagesBus speeds in London have slowed to their lowest level in years, causing a fall in passenger numbers, the London Assembly has heard.
Average speeds on the capital’s bus network fell to 9.17mph in 2024–25, down from 10.27mph four years earlier, according to City Hall data. In August, the latest month available, buses were travelling at 9.06mph on average.
Passenger numbers also fell last year for the first time since the pandemic, dropping from 1.869bn journeys to 1.842bn.
Transport for London (TfL) said its Bus Action Plan would speed up travel, with 15.5 miles (25km) of new bus lanes, 1,900 signals prioritising buses and 52.8 miles (85km) of existing lanes operating 24 hours a day.
The assembly’s transport committee was told this week slower services and “endless traffic” were making buses less attractive.
Paul Lynch, managing director of Stagecoach London, said conditions had “worsened over the last few years to a point where somebody who works for me… and has been around for 40 years operating buses in London says it’s the worst he has ever seen”.
He added: “It’s making them less attractive and less reliable… It’s got to be one of the reasons why bus passenger numbers are declining at the same time that bus speeds are.”
TfL’s latest Travel in London report recorded a 1.5% fall in bus journeys compared with last year, alongside rises in passenger numbers on the Underground and Elizabeth line.
Michael Roberts, chief executive of London TravelWatch, told members that slower journey times “mean reduced patronage, which in turn means reduced income to TfL”.
He said slower speeds also increased operating costs because “you need more buses to run a given level of service”, adding that buses are “an effective use of road space” and declining use was “bad for London”.
“For every 10% reduction in journey speeds, there’s a 6% reduction in demand,” he said.
London TravelWatch estimates that meeting the mayor’s aim for 80% of trips to be made by walking, cycling or public transport by 2041 would require bus journeys to rise by 40%
TfL analysis suggests daily trips must grow from 5.1m to 9m.
Some boroughs experience far slower services than others, with average speeds under 7mph in the City of London, Camden and Westminster.
Bexley, Hillingdon and Havering recorded average speeds above 11mph.

Antidepressants don’t have to be taken forever, a new analysis suggests.
Every year, a growing number of people across Europe take antidepressants to help treat symptoms related to depression and anxiety. While current guidelines recommend…
Almandouh, M., Alrahmawy, M. F., Eisa, M., Elhoseny, M. & Tolba, A. S. Ensemble based high performance deep learning models for fake news detection. Sci. Rep. 14, 26591. https://doi.org/10.1038/s41598-024-26591-5 (2024).
Praseed, A., Rodrigues, J. & Santhi, T. P. Disinformation detection using graph neural networks: A survey. Artif. Intell. Rev. 57 (2024).
Liu, J., Wu, F., Jin, H., Zhu, X. & Jing, X. Y. Inter-modal fusion network with graph structure preserving for fake news detection. In: Neural Information Processing. Springer, 1–9 https://doi.org/10.1007/978-981-99-8076-5_20 (2024).
Jiang, Y. & Liu, X. Deep learning for fake news detection: A survey. Artif. Intell. 303, 101235. https://doi.org/10.1016/j.artint.2022.101235 (2022).
Harris, S., Hadi, H. J., Ahmad, N. & Alshara, M. A. Fake news detection revisited: an extensive review of theoretical frameworks, dataset assessments, model constraints, and forward-looking research agendas. Technologies 12, 222. https://doi.org/10.3390/technologies12110222 (2024).
Wang, B., Feng, Y., Xiong, X. C., Wang, Y. H. & Qiang, B. H. Multi-modal transformer using two-level visual features for fake news detection. Appl. Intell. 53, 10429–10443. https://doi.org/10.1007/s10489-022-04055-5 (2022).
Lee, D. & Kim, S. Detecting fake news using social media and graph neural networks. J. Comput. Social Sci. 5, 125–137. https://doi.org/10.1007/s42001-021-00110-3 (2022).
Song, C., Teng, Y., Zhu, Y., Wei, S. & Bin, B. Dynamic graph neural network for fake news detection. Neurocomputing 505, 362–374. https://doi.org/10.1016/j.neucom.2022.07.013 (2022).
Xu, W., Wang, X. & Chen, Q. Leveraging attention-based networks for fake news detection in social media. IEEE Access. 10, 47115–47127. https://doi.org/10.1109/ACCESS.2022.3181273 (2022).
Roumeliotis, K. I., Tselikas, N. D. & Nasiopoulos, D. K. Fake news detection and classification: A comparative study of convolutional neural networks, large Language models, and natural Language processing models. Future Internet. 17, 28. https://doi.org/10.3390/fi17010028 (2025).
Folino, F., Folino, G., Guarascio, M. & Tagarelli, A. Towards data- and compute-efficient fake news detection: an approach combining active learning and pre-trained Language models. SN Comput. Sci. 5, 470. https://doi.org/10.1007/s42979-024-02809-1 (2024).
Sudhakar, M. & Kaliyamurthie, K. P. Efficient prediction of fake news using novel ensemble technique based on machine learning algorithm. In: Information and Communication Technology for Competitive Strategies. Springer, 1–10 https://doi.org/10.1007/978-981-19-0098-3_1 (2023).
Luo, P. & Xie, Y. Fake news detection via multi-task learning on graph networks. Inf. Sci. 613, 533–550. https://doi.org/10.1016/j.ins.2022.09.040 (2023).
Xu, X., Sun, C. & Wang, S. Fake news detection via multi-view graph convolutional network. ACM Trans. Inform. Syst. 41, 47. https://doi.org/10.1145/3458986.3458991 (2023).
Zhang, X. & Zhao, Q. Fake news detection using graph neural networks: A comprehensive survey. Neural Comput. Appl. https://doi.org/10.1007/s00542-023-08257-w (2023).
Rani, S. & Kumar, M. Multi-modal topic modeling from social media data using deep transfer learning. Appl. Soft Comput. 160, 111706. https://doi.org/10.1016/j.asoc.2024.111706 (2024).
Kumar, Y., Bhardwaj, P., Shrivastav, S. & Mehta, K. Predicting paediatric brain disorders from MRI images using advanced deep learning techniques. Neuroinformatics 23 (2), 9. https://doi.org/10.1007/s12021-024-09707-0 (2025).
Modi, N. et al. Physiological signal-based mental stress detection using hybrid deep learning models. Discover Artif. Intell. 5, 166. https://doi.org/10.1007/s44163-025-00412-8 (2025).
Alzahrani, M. A. & Aljuhani, M. A. Enhancing fake news detection with word embedding: A machine learning and deep learning approach. Computers 13, 239. https://doi.org/10.3390/computers13090239 (2024).
Zamani, A. S., Hashim, A. H. A., Mohamed, S. S. I. & Alam, M. N. Optimized deep learning techniques to identify rumors and fake news in online social networks. J. Comput. Cogn. Eng. 2, 1–12. https://doi.org/10.47852/bonviewJCCE52023348 (2023).
Wei, L. & Zhang, T. Fake news detection using deep learning-based fusion of graph convolutional networks and transformer models. Inform. Fusion. 81, 146–159. https://doi.org/10.1016/j.inffus.2022.11.004 (2023).
Abduljaleel, I. Q. & Ali, I. H. Deep learning and fusion mechanism-based multimodal fake news detection methodologies: A review. Eng. Technol. Appl. Sci. Res. 14, 15665–15675. https://doi.org/10.48084/etasr.7907 (2024).
Li, H., Liu, L. & Wang, Y. Fake news detection using a transformer-based framework with attention mechanisms. Expert Syst. Appl. 182, 115129. https://doi.org/10.1016/j.eswa.2021.115129 (2022).
Jing, J., Wu, H., Sun, J., Fang, X. & Zhang, H. Multimodal fake news detection via progressive fusion networks. Inf. Process. Manag. 60, 103120. https://doi.org/10.1016/j.ipm.2022.103120 (2023).
Dixit, D. K., Bhagat, A. & Dangi, D. An accurate fake news detection approach based on a levy flight honey Badger optimized convolutional neural network model. Concurrency Computation: Pract. Experience. 35, e7382. https://doi.org/10.1002/cpe.7382 (2023).
Yang, L. & Lee, K. Fake news detection in social media using a hybrid model of deep neural networks. Neural Netw. 145, 202–213. https://doi.org/10.1016/j.neunet.2021.09.003 (2022).
Singhania, S., Fernandez, N. & Rao, S. 3HAN: A deep neural network for fake news detection. ArXiv Preprint (2023). ArXiv:2306.12014
Kikon, J. M. & Bania, R. K. Towards development of machine learning models for fake news detection and sentiment analysis. In: Proceedings of the NIELIT’s International Conference on Communication, Electronics and Digital Technology (NICEDT 2024). pp. 99–107 https://doi.org/10.1007/978-981-97-3601-0_8 (Springer, Singapore, 2024).
Fu, X., Guo, C. & Yuan, Z. A survey of fake news detection in social media: Methods, trends, and challenges. Computers 12, 80. https://doi.org/10.3390/computers12050080 (2023).
Patel, R. & Gupta, P. Fake news detection via text and graph-based fusion learning. Comput. Intell. Neurosci. https://doi.org/10.1155/2022/6574754 (2022).
Truică, C. O., Apostol, E. S., Marogel, M., Paschke, A. & GETAE Graph information enhanced deep neural network ensemble architecture for fake news detection. Expert Syst. Appl. 275, 126984. https://doi.org/10.1016/j.eswa.2025.126984 (2025).
Zhang, L. & Chen, X. An improved method for fake news detection using attention-based neural networks. Appl. Soft Comput. 115, 108273. https://doi.org/10.1016/j.asoc.2021.108273 (2022).
Papageorgiou, E., Varlamis, I. & Chronis, C. Harnessing large Language models and deep neural networks for fake news detection. Information 16, 297. https://doi.org/10.3390/info16040297 (2025).
Jin, W. et al. Veracity-oriented context-aware large Language models–based prompting optimization for fake news detection. Int. J. Intell. Syst. 40, 5920142. https://doi.org/10.1002/int.5920142 (2025).
Jin, W. et al. -FND: A multi-role fake news detection method based on argument switching-based courtroom debate. J. King Saud Univ. – Comput. Inform. Sci. 37, 33. https://doi.org/10.1016/j.jksuci.2024.101033 (2025).
Khattar, D., Goud, J. S., Gupta, M. & Mvae, V. V. Multimodal variational autoencoder for fake news detection. In: The World Wide Web Conference, pp. 2915–2921 (2019).
Jin, W. et al. A veracity dissemination consistency-based few-shot fake news detection framework by synergizing adversarial and contrastive self-supervised learning. Sci. Rep. 14, 19470. https://doi.org/10.1038/s41598-024-19470-0 (2024).
Jin, W. et al. A prompting multi-task learning-based veracity dissemination consistency reasoning augmentation for few-shot fake news detection. Eng. Appl. Artif. Intell. 144, 110122. https://doi.org/10.1016/j.engappai.2025.110122 (2025).
Abulaish, M., Kamal, A. & Zaki, M. J. A survey of figurative Language and its computational detection in online social networks. ACM Trans. Web. 14, 1–52. https://doi.org/10.1145/3383212 (2020).
Kamal, A. & Abulaish, M. Contextualized satire detection in short texts using deep learning techniques. J. Web Eng. 23, 27–52 (2024).
Abulaish, M. & Kamal, A. Self-deprecating sarcasm detection: An amalgamation of rule-based and machine learning approach. In: 2018 IEEE/WIC/ACM International Conference on Web Intelligence (WI). pp. 415–422 https://doi.org/10.1109/WI.2018.00-25 (IEEE, 2018).
Kamal, A., Anwar, T., Sejwal, V. K., Fazil, M. & BiCapsHate Attention to the linguistic context of hate via bidirectional capsules and hatebase. IEEE Trans. Comput. Social Syst. 11, 1781–1792. https://doi.org/10.1109/TCSS.2022.3228775 (2023).
Mohankumar, P., Kamal, A., Singh, V. K. & Satish, A. Financial fake news detection via context-aware embedding and sequential representation using cross-joint networks. In: 2023 15th International Conference on Communication Systems & Networks (COMSNETS), pp. 780–784 https://doi.org/10.1109/COMSNETS56262.2023.10041125 (IEEE, 2023).
Kamal, A., Mohankumar, P. & Singh, V. K. Financial misinformation detection via RoBERTa and multi-channel networks. In: International Conference on Pattern Recognition and Machine Intelligence. pp. 646–653 https://doi.org/10.1007/978-3-031-40375-8_53 (Springer, Cham, 2023).
Ilie, V. I., Truică, C. O., Apostol, E. S. & Paschke, A. Context-aware misinformation detection: A benchmark of deep learning architectures using word embeddings. IEEE Access. 9, 162122–162146. https://doi.org/10.1109/ACCESS.2021.3132502 (2021).
Truică, C. O., Apostol, E. S. & MisRoBÆRTa Transformers versus misinformation. Mathematics 10, 569. https://doi.org/10.3390/math10040569 (2022).
Truică, C. O. & Apostol, E. S. Fake news detection using sentence Transformers. CEUR Workshop Proc. 3180, paper–61 (2022). https://ceur-ws.org/Vol-3180/paper-61.pdf
Truică, C. O. & Apostol, E. S. It’s all in the embedding! Fake news detection using document embeddings. Mathematics 11, 508. https://doi.org/10.3390/math11030508 (2023).
Petrescu, A., Truică, C. O. & Apostol, E. S. Language-based mixture of Transformers for EXIST2024. CEUR Workshop Proc. 3740, paper–108 (2024). https://ceur-ws.org/Vol-3740/paper-108.pdf
Truică, C. O., Apostol, E. S. & Karras, P. Deep neural network ensemble architecture for social and textual context-aware fake news detection. Knowl. Based Syst. 294, 111715. https://doi.org/10.1016/j.knosys.2024.111715 (2024).
E Almandouh M, Alrahmawy MF, Eisa M, Elhoseny M, Tolba AS. Ensemble based highperformance deep learning models for fake news detection. Scientific Reports 14 (1), 26591 (2024).
Ghosh, A. et al. Proactive network immunization for misinformation control. In: Proceedings of the ACM Conference on Computer and Communications Security (CCS), pp. xxx–xxx https://doi.org/10.1145/3459637.3482481 (2021).
Truică, C. O., Apostol, E. S., Nicolescu, R. C. & Karras, P. M. C. W. D. S. T. A minimum-cost weighted directed spanning tree algorithm for real-time fake news mitigation in social media. IEEE Access. 11, 125861–125873. https://doi.org/10.1109/ACCESS.2023.3331220 (2023).
Apostol, E. S., Coban, Ö. & Truică, C. O. CONTAIN: A community-based algorithm for network immunization. Eng. Sci. Technol. Int. J. 55, 101728. https://doi.org/10.1016/j.jestch.2024.101728 (2024).
Apostol, E. S., Truică, C. O., Paschke, A. & ContCommRTD A distributed content-based misinformation-aware community detection system for real-time disaster reporting. IEEE Trans. Knowl. Data Eng. 36, 5811–5822. https://doi.org/10.1109/TKDE.2024.3417232 (2024).
Truică, C. O., Constantinescu, A. T. & Apostol, E. S. StopHC: A harmful content detection and mitigation architecture for social media platforms. In: Proceedings of the IEEE International Conference on Intelligent Computer Communication and Processing (ICCP), pp. xxx–xxx https://doi.org/10.1109/ICCP63557.2024.10793051 (2024).
Almeida, F. et al. Virality detection in social media. In: Proceedings of the International Conference on Extending Database Technology (EDBT), pp. xxx–xxx https://doi.org/10.5441/002/edbt.2021.69 (2021).
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Denis Balibouse | Reuters
Big Tech is doubling down on investing billions in India, drawn by its abundance of resources for building data centers, a large talent and digital user pool, and market opportunity.
In under 24 hours, Microsoft and Amazon pledged more than $50 billion toward India’s cloud and AI infrastructure, while Intel on Monday announced plans to make chips in the country to capitalize on its growing PC demand and speedy AI adoption.
While India trails the U.S. and China in the race to develop a native AI foundational model, and lacks a large domestic AI infrastructure company, it wants to leverage its expertise in the information technology sector to create and deploy AI applications at enterprise level, also offering Big Tech companies a huge opportunity.
Having a model or computing is not enough for any enterprise to use AI effectively, and it requires companies making application layer and a large talent pool to deploy them, S. Krishnan, secretary at India’s Ministry of Electronics and Information Technology, told CNBC.
Stanford University ranks India among the top four countries along with the U.S., China and the UK in the global and national AI vibrancy ranking. GitHub, a community of developers, has ranked India at the top with the global share of 24% of all projects.
India’s opportunity lies more in “developing applications” which will be used to drive revenues for AI companies, Krishnan said.
On Tuesday, Microsoft announced $17.5 billion in investment in the country, spread over 4 years, aimed at expanding hyperscale infrastructure, embedding AI into national platforms, and advancing workforce readiness.
“This scale of capex gives Microsoft first‑mover advantage in GPU‑rich data centers while making Azure the preferred platform for India’s AI workloads, as well as deepening alignment with the government’s AI public infrastructure push,” said Tarun Pathak, research Director at Counterpoint Research.
Amazon on Wednesday announced plans to invest over $35 billion, on top of the $40 billion it has already invested in the country.
Over the past few months, AI and tech majors such as OpenAI, Google, and Perplexity have offered their tools for free to millions in India, with Google also firming up its plans to invest $15 billion toward building data center capacity for a new AI hub in southern India.
“India combines a huge digital user base, rapidly growing cloud and AI demand, and a high-talent IT ecosystem that can build and consume AI at scale, making it more than just a market for users and instead a core engineering and deployment hub,” Pathak said.
India has several advantages when it comes to building data centers. Markets such as Japan, Australia, China and Singapore in the Asia Pacific region have matured. Singapore, one of the oldest data center hubs in the region, has limited room to deploy large-scale data centers due to land availability issues.
India has abundant space for large-scale data center developments. When compared with data center hubs in Europe, power costs in India are relatively low. Coupled with India’s growing renewable energy capacity — critical for power-hungry data centers — and the economics begin to look compelling.
Local demand, fueled by the rise of e-commerce — a major driver of data center growth in recent years — and potential new rules for storing social media data, strengthens the case.
Put simply: India is entering a sweet spot where global cloud providers, AI players, and domestic digitalization all converge to create one of the world’s hottest data center markets.
“India is a pivotal market and one of the fastest‑growing regions for AI spending in Asia Pacific,” said Deepika Giri, associate vice president and head of research, big data & AI, at International Data Corporation.
“A major gap, and therefore a significant opportunity, lies in the shortage of suitable compute infrastructure for running AI models,” she added. Big Tech is looking to capitalize on the infrastructure opportunity in India by investing heavily in the cloud and data center space.
Global companies are expanding capacities closer to service bases in IT cities such as Bangalore, Hyderabad and Pune from traditional centers like Mumbai and Chennai which are closer to landing cables, as they build data centers in India for the world, Krishnan said.
— CNBC’s Dylan Butts, Amitoj Singh contributed to this report.