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House Speaker Kevin McCarthy (R-Calif.) lashed out against California Democratic Reps. Eric Swalwell and Adam Schiff on Jan. 12. He levied unsubstantiated claims as reasons for barring them from committee assignments.

House Speaker Kevin McCarthy (R-Calif.) on Jan. 24 said that he would block California Democratic Reps. Adam B. Schiff and Eric Swalwell from serving on the House Intelligence Committee.

Sen. James Lankford (R-Okla.) criticized lead House impeachment manager Rep. Adam B. Schiff (D-Calif.) on Jan. 24.

On March 5 voters head to the polls to decide two key Senate races in California and in Texas.

During a campaign rally on Jan. 9 in Toledo, President Trump lashed out at Rep. Adam B. Schiff (D-Calif.) over Democrat complaints on Iran policy.

A normal congressional hearing can make for great television (think of Adam Schiff and Doug Collins locking horns for the first Trump impeachment, or the Brett Kavanaugh confirmation hearings). If the January 6 hearings can't foster a sense of participation in a public ritual, they stand little chance of becoming part of America's political life, or of shaping political opinions, as the Watergate hearings did in the early 1970s. The committee makes a classic mistake: trying to address a problem with the form by intensifying the content. Since the committee took the time and effort to produce a big TV special, the members need to fight the suspicion that what they're communicating is not urgent. Each episode, explicating one part of the \"seven-part plan,\" reveals an institution the president failed to get on his side: the VP's office, state officials, the Department of Justice, the Secret Service, even-in the tale of former White House staffer Cassidy Hutchinson-the presidential limo driver.

President Trump said House Intelligence Committee chairman Rep. Adam B. Schiff (D-Calif.) is launching investigations to \"build a name for himself.\"

Influencing and framing debates on Twitter provides power to shape public opinion. Bots have become essential tools of ‘computational propaganda’ on social media such as Twitter, often contributing to a large fraction of the tweets regarding political events such as elections. Although analyses have been conducted regarding the first impeachment of former president Donald Trump, they have been focused on either a manual examination of relatively few tweets to emphasize rhetoric, or the use of Natural Language Processing (NLP) of a much larger corpus with respect to common metrics such as sentiment. In this paper, we complement existing analyses by examining the role of bots in the first impeachment with respect to three questions as follows. (Q1) Are bots actively involved in the debate? (Q2) Do bots target one political affiliation more than another? (Q3) Which sources are used by bots to support their arguments? Our methods start with collecting over 13M tweets on six key dates, from October 6th 2019 to January 21st 2020. We used machine learning to evaluate the sentiment of the tweets ( via BERT ) and whether it originates from a bot. We then examined these sentiments with respect to a balanced sample of Democrats and Republicans directly relevant to the impeachment, such as House Speaker Nancy Pelosi, senator Mitch McConnell, and (then former Vice President) Joe Biden. The content of posts from bots was further analyzed with respect to the sources used (with bias ratings from AllSides and Ad Fontes) and themes. Our first finding is that bots have played a significant role in contributing to the overall negative tone of the debate (Q1). Bots were targeting Democrats more than Republicans (Q2), as evidenced both by a difference in ratio (bots had more negative-to-positive tweets on Democrats than Republicans) and in composition (use of derogatory nicknames). Finally, the sources provided by bots were almost twice as likely to be from the right than the left, with a noticeable use of hyper-partisan right and most extreme right sources (Q3). Bots were thus purposely used to promote a misleading version of events. Overall, this suggests an intentional use of bots as part of a strategy, thus providing further confirmation that computational propaganda is involved in defining political events in the United States. As any empirical analysis, our work has several limitations. For example, Trump’s rhetoric on Twitter has previously been characterized by an overly negative tone, thus tweets detected as negative may be echoing his message rather than acting against him. Previous works show that this possibility is limited, and its existence would only strengthen our conclusions. As our analysis is based on NLP, we focus on processing a large volume of tweets rather than manually reading all of them, thus future studies may complement our approach by using qualitative methods to assess the specific arguments used by bots.

President Trump spoke to reporters before departing the White House Feb. 23, claiming he had not been briefed on reports alleging Russia wants Sen. Bernie Sanders (I-Vt.) to win the Democratic presidential primary.

A day after President Trump's impeachment legal team previewed its defense, lawmakers diverged Jan. 26 on whether to add witnesses and the tone of proceedings.