The Web Conference

Dated Apr 19, 2021; last modified on Fri, 07 Jan 2022

The Web Conference is slated to be a part of ACM’s SIGWEB from 2022 . The papers in WWW also cover topics that are not directly related to the web, e.g. recommendations, machine learning, etc.

ACM’s Special Interest Groups seem pretty important, so they should be a good source of current ideas. The annual Hypertext and Social Media Conference (HT) should be a good supplement to WWW. Browsing HT ‘21 reveals that it has more web-related topics, but the number of submissions is much lower than that of WWW ‘21.

The Web Conference 2021

The Web Conference 2021. dl.acm.org . Apr 19, 2021. ISBN: 9781450383127 .

WWW'19 had 297 full & short papers (18.47% acceptance rate) , and Scimago calculated a h-index of 22 . Assuming a similar trend, out of the 355 papers in WWW ‘21, I expect 26+ papers with 22+ citations.

		Random Link ¯\_(ツ)_/¯
Jan 23, 2021	»	Research on Privacy Enhancing Techniques 2 min; updated Feb 12, 2023 Journals note that prediction services can still make accurate predictions using a fraction of the data collected from a user device. They propose Cloak, which suppresses non-pertinent features (i.e. those features which can consistently tolerate addition of noise without degrading utility) to the prediction task. Cloak has a provable degree of privacy, and unlike cryptographic techniques, does not degrade prediction latency. Using the training data, labels, a pre-trained model and a privacy-utility knob, they (1) find the pertinent features through perturbation training, and (2) learn utility-preserving constant values for suppressing the non-pertinent data. During the inference phase, they efficiently compute a sifted representation which is sent to the service provider. ...
Oct 4, 2021	»	Journal Reviews on Fairness 7 min; updated Feb 12, 2023 Meta Instead of changing the data or learners in multiple ways and then see if fairness improves, postulate that the root causes of bias are the prior decisions that generated the training data. These affect (a) what data was selected, and (b) the labels assigned to the examples. They propose the \(\text{Fair-SMOTE}\) (Fair Synthetic Minority Over Sampling Technique) algorithm which (1) removes biased labels (via situation testing: if the model’s prediction for a data point changes once all of the data points' protected attributes are flipped, then that label is biased and the data point is discarded), and (2) rebalances internal distributions such that based on a protected attribute, examples are equal in both positive and negative classes. The method is just as effective in reducing bias as prior approaches, and its models achieve higher recall and F1 performance. Furthermore, \(\text{Fair-SMOTE}\) can simultaneously reduce bias for more than one protected attribute. ...
Oct 13, 2021	»	Towards a Collaborative Web Browser 3 min; updated Feb 12, 2023 One of the grand dreams of the web is bringing people together so that they may find relevant information. Sometimes this is implicit, e.g. Google Search’s PageRank learns from inter-page links which are mostly made by people, but there may be room for explicit collaboration when navigating the web. This section explores such explicit attempts. #lost-in-hyperspace A Group Asynchronous Browsing (GAB) Server Premise: Users submit their subject hierarchies (bookmarks) to the GAB server. The GAB server then forms a multitree from these hierarchies. ...
Jan 11, 2022	»	Journal Reviews 5 min; updated Feb 12, 2023 Link Prediction Given network-structured data, predict whether a link exists between two nodes. General types of prediction tasks on graphs: graph-level (e.g. will a molecule bind to a receptor implicated in a disease?), node-level (e.g. what is the identity of each node?), and edge-level (e.g. does this edge exist; what value does it have?). Applications include: predicting drug-drug interactions (common in treating patients with complex/co-existing diseases) as they may cause changes in the drugs’ pharmacological activity . ...
Jan 27, 2020	»	Tech and Democracy 5 min; updated Sep 5, 2022 Political Ads Cambridge Analytica (CA) paid people to take in-app survey; mined FB profile data including friends’ data; crafted tailored sensitive ads to sway-able voters. Elections are about emotions, not facts. Data science and social media can help us make sense of and manipulate the chaos. An alternative argument. Political misinformation is: Weak in high profile partisan races because pre-existing beliefs hardly change Strong when people don’t have string pre-existing opinions, e.g. misinformation about voter ID laws causing people to stay home from the polls ...
Oct 4, 2021	»	Online Markets 4 min; updated Sep 5, 2022 WWW ‘21: The Web Conference 2021 REST: Relational Event-Driven Stock Trend Forecasting REST, an event-driven stock trend forecasting framework, that overcomes two limitations of existing event-driven models. Models the stock context, and learns the effect of event information on the stocks under different contexts. Constructs a stock graph and designs a new propagation layer to propagate the effect of event information from related stocks. #stock-trend-forecasting #computational-finance #graph-based-learning The value of stock trend forecasting is not unanimous, e.g. Malkiel contends that forecasting is a fool’s game , while Simon’s RenTech is all about the math . But it seems like the question is an empirical one, and therefore, a good answer should exist. Why are there opposing camps? ...

The Web Conference 2021

Journals

Meta

A Group Asynchronous Browsing (GAB) Server

Link Prediction

Political Ads

WWW ‘21: The Web Conference 2021

REST: Relational Event-Driven Stock Trend Forecasting